In [459]:
import cv2
import matplotlib.pyplot as plt
import glob
import numpy as np
%matplotlib inline
In [658]:
def camera_calibration():
"""
Function uses chess board images to calculate
calibration coefficients and returns mtx, dist
"""
objpoints = []
imgpoints = []
objp = np.zeros((6*9,3),np.float32)
objp[:,:2] = np.mgrid[0:9,0:6].T.reshape(-1,2)
url = '../CarND-Advanced-Lane-Lines/camera_cal/'
name = 'calibration*.jpg'
images = glob.glob(url+name)
if len(images)>0:
for image in images:
img = cv2.imread(image)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, corners = cv2.findChessboardCorners(gray, (9, 6), None)
if ret == True:
imgpoints.append(corners)
objpoints.append(objp)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)
return mtx, dist
else:
return None, None
mtx, dist = camera_calibration()
In [659]:
def undist (img):
"""
Function takes an image and mske distortion correction
"""
return cv2.undistort(img, mtx, dist, None, mtx)
def abs_sobel_thresh(img, orient='x', thresh_min=0, thresh_max=255):
"""
Function takes an image, grayscale it, applies Sobel x or y,
then takes an absolute value and applies a threshold.
"""
# 1) Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# 2) Take the derivative in x or y given orient = 'x' or 'y'
if orient!='x':
sobel_x = 0
sobel_y = 1
else:
sobel_x = 1
sobel_y = 0
sobel = cv2.Sobel(gray, cv2.CV_64F, sobel_x, sobel_y)
# 3) Take the absolute value of the derivative or gradient
abs_sobel = np.absolute(sobel)
# 4) Scale to 8-bit (0 - 255) then convert to type = np.uint8
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# 5) Create a mask of 1's where the scaled gradient magnitude
# is > thresh_min and < thresh_max
binary_output = np.zeros_like(scaled_sobel)
binary_output[(scaled_sobel>thresh_min)&(scaled_sobel<=thresh_max)]=1
# 6) Return this mask as your binary_output image
return binary_output
def mag_thresh(img, sobel_kernel=3, mag_thresh=(0, 255)):
"""
Function applies Sobel x and y, then computes the magnitude of the gradient
and applies a threshold
"""
# 1) Convert to grayscale
gray = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
# 2) Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# 3) Calculate the magnitude
abs_sobelxy = (sobelx**2+sobely**2)**0.5
# 4) Scale to 8-bit (0 - 255) and convert to type = np.uint8
scaled_sobelxy = np.uint8(255*abs_sobelxy/np.max(abs_sobelxy))
# 5) Create a binary mask where mag thresholds are met
binary_output = np.zeros_like(scaled_sobelxy)
binary_output[(scaled_sobelxy>mag_thresh[0])&(scaled_sobelxy<mag_thresh[1])] = 1
# 6) Return this mask as your binary_output image
return binary_output
def dir_threshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
"""
Function applies Sobel x and y, then computes the direction of the gradient
and applies a threshold.
"""
# 1) Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# 2) Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize = sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize = sobel_kernel)
# 3) Take the absolute value of the x and y gradients
abs_sobelx = np.absolute(sobelx)
abs_sobely = np.absolute(sobely)
# 4) Use np.arctan2(abs_sobely, abs_sobelx) to calculate the direction of the gradient
direction = np.arctan2(abs_sobely, abs_sobelx)
# 5) Create a binary mask where direction thresholds are met
binary_output = np.zeros_like(direction)
binary_output[(direction>thresh[0])&(direction<thresh[1])] = 1
# 6) Return this mask as your binary_output image
return binary_output
def binary_and(binary1, binary2):
"""
Function takes to binary images same shape and return their intersection
"""
if binary1.shape == binary2.shape:
binary = np.where(np.logical_and(binary1,binary2)==True, 1., 0.)
return binary
else:
return None
def binary_or(binary1, binary2):
"""
Function takes to binary images same shape and return their union
"""
if binary1.shape == binary2.shape:
binary = np.where(np.logical_or(binary1,binary2)==True, 1., 0.)
return binary
else:
return None
def binary_xor(binary1, binary2):
"""
Function takes to binary images same shape and return their union
"""
if binary1.shape == binary2.shape:
binary = np.where(np.logical_xor(binary1,binary2)==True, 1., 0.)
return binary
else:
return None
def ch_threshold(img, ch=0, thresh=(0,180)):
"""
Function takes an image (RGB, HSV, etc.),
channel (0,1,2) for 3 channels, threshold
and returns binary image of the thresholded channel
"""
if img.shape[2]>ch:
CH = img[:,:,ch]
binary = np.zeros_like(CH)
binary [(CH>thresh[0])&(CH<=thresh[1])] = 1
return binary
else:
return None
def show_2gr(img1,img2):
"""
Function takes 2 images and plot them next to each other.
"""
f, (ax1,ax2) = plt.subplots(1,2,figsize=(20,10))
ax1.imshow(img1, cmap='gray')
ax2.imshow(img2, cmap='gray')
def warp(img):
"""
Function takes an image and return an image with perspective transformation applied.
Src points were taken on the images, provided by Udacity. Depends from the camera.
Proportion is not saved: height ~22.5m, width ~7.4m
"""
w,h = img.shape[1],img.shape[0]
img_size=(w,h)
y = 0.8*h
#x = 3.7/18*y
x=0.5*w
src = np.float32([[434./1280*w,562./720*h],
[589./1280*w,453./720*h],
[694./1280*w,453./720*h],
[864./1280*w,562./720*h]])
dst = np.float32([[(w-x)/2.,0.9*h],
[(w-x)/2.,0.1*h],
[(w+x)/2.,0.1*h],
[(w+x)/2.,0.9*h]])
M = cv2.getPerspectiveTransform(src,dst)
img_warp = cv2.warpPerspective(img,M,img_size,flags=cv2.INTER_LINEAR)
return img_warp
def unwarp(img):
"""
Function takes warped image and returns unwarped image
"""
w,h = img.shape[1],img.shape[0]
img_size=(w,h)
y = 0.8*h
#x = 3.7/18*y
x=0.5*w
dst = np.float32([[434./1280*w,562./720*h],
[589./1280*w,453./720*h],
[694./1280*w,453./720*h],
[864./1280*w,562./720*h]])
src = np.float32([[(w-x)/2.,0.9*h],
[(w-x)/2.,0.1*h],
[(w+x)/2.,0.1*h],
[(w+x)/2.,0.9*h]])
M = cv2.getPerspectiveTransform(src,dst)
img_warp = cv2.warpPerspective(img,M,img_size,flags=cv2.INTER_LINEAR)
return img_warp
def hist(img):
"""
Function takes binary image and returns histogram:
sum of "1" pixel for each x on the bottom half of the image
"""
histogram = np.sum(img[img.shape[0]/2:,:], axis=0)
return histogram
Below I'm applying distortion correction to the image. At the left is original image, at the right - corrected.
In [661]:
fname = '/Users/olegair/Documents/MyNewCareer/SDCND/p4/CarND-Advanced-Lane-Lines/test_images/straight_lines1.jpg'
img = cv2.cvtColor(cv2.imread(fname),cv2.COLOR_BGR2RGB)
dst = undist(img)
f, (ax1,ax2) = plt.subplots(1,2,figsize=(20,10))
ax1.imshow(img)
ax2.imshow(dst)
Out[661]:
<matplotlib.image.AxesImage at 0x34a888320>
Below I'm going through the process of applying thresholds on different options of image gradient
In [677]:
fname = '/Users/olegair/Documents/MyNewCareer/SDCND/p4/CarND-Advanced-Lane-Lines/test_images/test5.jpg'
img = cv2.cvtColor(cv2.imread(fname),cv2.COLOR_BGR2RGB)
img = undist(img)
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1)
abs_sobelx = np.absolute(sobelx)
abs_sobely = np.absolute(sobelx)
scaled_sobelx = np.uint8(255*abs_sobelx/np.max(abs_sobelx))
scaled_sobely = np.uint8(255*abs_sobely/np.max(abs_sobely))
sxbinary = abs_sobel_thresh(img, orient='x', thresh_min=20, thresh_max=100)
sybinary = abs_sobel_thresh(img, orient='y', thresh_min=20, thresh_max=100)
mag_binary = mag_thresh(img, sobel_kernel=31, mag_thresh=(30, 100))
dir_binary = dir_threshold(img, sobel_kernel=15, thresh=(0.7, 1.3))
f, axarr = plt.subplots(6,2, figsize=(20,40))
axarr[0,0].imshow(img)
axarr[0,0].set_title('Original image')
axarr[0,1].imshow(gray,cmap='gray')
axarr[0,1].set_title('Grayscale image')
axarr[1,0].imshow(sobelx,cmap='gray')
axarr[1,0].set_title('Sobel operator X')
axarr[1,1].imshow(sobely,cmap='gray')
axarr[1,1].set_title('Sobel operator Y')
axarr[2,0].imshow(abs_sobelx,cmap='gray')
axarr[2,0].set_title('Sobel operator X, absolute')
axarr[2,1].imshow(abs_sobely,cmap='gray')
axarr[2,1].set_title('Sobel operator Y, absolute')
axarr[3,0].imshow(scaled_sobelx,cmap='gray')
axarr[3,0].set_title('Sobel operator X, scaled')
axarr[3,1].imshow(scaled_sobely,cmap='gray')
axarr[3,1].set_title('Sobel operator Y, scaled')
axarr[4,0].imshow(sxbinary,cmap='gray')
axarr[4,0].set_title('Sobel operator X, scaled, thresholded')
axarr[4,1].imshow(sybinary,cmap='gray')
axarr[4,1].set_title('Sobel operator Y, scaled, thresholded')
axarr[5,0].imshow(mag_binary,cmap='gray')
axarr[5,0].set_title('Magnitude of the Gradient, thresholded')
axarr[5,1].imshow(dir_binary,cmap='gray')
axarr[5,1].set_title('Direction of the Gradient, thresholded')
Out[677]:
<matplotlib.text.Text at 0x22218c908>
And some experiments of combining binary images with operations logical and, or, xor.
In [683]:
sx_sy = binary_and(sybinary, sxbinary)
mag_dir = binary_and(mag_binary, dir_binary)
mag_sx = binary_xor(mag_binary,sxbinary)
mag_sx_mag_dir = binary_and(mag_sx,mag_dir)
f, axarr = plt.subplots(2,2,figsize=(20,10))
axarr[0,0].imshow(sx_sy, cmap='gray')
axarr[0,0].set_title('Sobel X AND Sobel Y')
axarr[0,1].imshow(mag_dir, cmap='gray')
axarr[0,1].set_title('Magnitude AND Direction')
axarr[1,0].imshow(mag_sx, cmap='gray')
axarr[1,0].set_title('Magnitude XOR Sobel X')
axarr[1,1].imshow(mag_sx_mag_dir, cmap='gray')
axarr[1,1].set_title('(Magnitude XOR Sobel X) AND (Magnitude AND Direction)')
Out[683]:
<matplotlib.text.Text at 0x251093eb8>
HueLighteningSaturation cylindrical-coordinate representations of RGB image¶
Below I'm experimenting with channels of HLS image representation
Visualization of Saturation channel
In [693]:
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
s_th = ch_threshold(hls, 2, thresh=(170,255))
f, ax = plt.subplots(1,2,figsize=(20,10))
ax[0].imshow(img)
ax[0].set_title('Original image')
ax[1].imshow(s_th, cmap='gray')
ax[1].set_title('Saturation channel of the image, thresholded')
Out[693]:
<matplotlib.text.Text at 0x1e9e3bb00>
Visualization of Hue channel
In [694]:
# binary hue
h_th = ch_threshold(hls, 0, thresh=(15,100))
f, ax = plt.subplots(1,2,figsize=(20,10))
ax[0].imshow(img)
ax[0].set_title('Original image')
ax[1].imshow(h_th, cmap='gray')
ax[1].set_title('Hue channel of the image, thresholded')
Out[694]:
<matplotlib.text.Text at 0x2a5791630>
Union of Saturation and Hue channels
In [695]:
s_or_h = binary_or(h_th,s_th)
f,ax = plt.subplots(1,3,figsize=(20,10))
ax[0].imshow(s_th,cmap='gray')
ax[0].set_title('Saturation channel, thresholded')
ax[1].imshow(h_th,cmap='gray')
ax[1].set_title('Hue channel, thresholded')
ax[2].imshow(s_or_h,cmap='gray')
ax[2].set_title('Hue channel OR Saturation channel, thresholded')
Out[695]:
<matplotlib.text.Text at 0x2d0d1b390>
Union of applied Soebel operator in X direction and Saturation channel.
In [696]:
s_sx = binary_or(sxbinary, s_th)
f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 10))
f.tight_layout()
ax1.imshow(sxbinary, cmap='gray')
ax1.set_title('Sobel operator, X')
ax2.imshow(s_th, cmap='gray')
ax2.set_title('Saturation (HLS)')
ax3.imshow(s_sx, cmap='gray')
ax3.set_title('Soebel X OR Saturation (HLS)')
Out[696]:
<matplotlib.text.Text at 0x25286dfd0>
HueSaturationValue representation of the RGB image¶
In [697]:
hsv = cv2.cvtColor(img,cv2.COLOR_RGB2HSV)
H_hsv = hsv[:,:,0]
S_hsv = hsv[:,:,1]
V_hsv = hsv[:,:,2]
f, (ax1) = plt.subplots(1,1)
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image')
f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(24, 9))
f.tight_layout()
ax1.imshow(H_hsv, cmap='gray')
ax1.set_title('H', fontsize=50)
ax2.imshow(S_hsv, cmap='gray')
ax2.set_title('S', fontsize=50)
ax3.imshow(V_hsv, cmap='gray')
ax3.set_title('V', fontsize=50)
Out[697]:
<matplotlib.text.Text at 0x220a56438>
Perspective transformation of the binary image¶
In [698]:
lines_persp = warp(s_sx)
show_2gr(s_sx,lines_persp)
Calculating histogram on warped image
In [702]:
histogram = hist(lines_persp)
plt.imshow(lines_persp,cmap='gray')
plt.plot(img.shape[0]-histogram)
Out[702]:
[<matplotlib.lines.Line2D at 0x266723f60>]
In [703]:
def first_lines(lines_persp, debug=0):
"""
Function takes warped image and find the lane lines using
histogram and sliding window. Returns coef-s of left's and
right's lines second order polynoms
"""
histogram = hist(lines_persp)
mid_point = int(len(histogram)/2)
left = int(np.argmax(histogram[:mid_point]))
right = int(np.argmax(histogram[mid_point:])) + mid_point
h = lines_persp.shape[0]
w = lines_persp.shape[1]
margin = 80
num_wind = 9
height = int(lines_persp.shape[0] / num_wind)
left_line = [[],[]]
right_line = [[],[]]
for window in range(num_wind):
y_low = h-(window+1)*height
y_high = h-window*height
left_x_low = left-margin
left_x_high = left+margin
right_x_low = right-margin
right_x_high = right+margin
left_box = np.nonzero(lines_persp[y_low:y_high, left_x_low:left_x_high])
left_box_y = np.array(left_box[0])+y_low
left_box_x = np.array(left_box[1])+left_x_low
left_line[0].append(left_box_x)
left_line[1].append(left_box_y)
right_box = np.nonzero(lines_persp[y_low:y_high, right_x_low:right_x_high])
right_box_y = np.array(right_box[0])+y_low
right_box_x = np.array(right_box[1])+right_x_low
right_line[0].append(right_box_x)
right_line[1].append(right_box_y)
if len(left_box[1])>50:
left = left_x_low+int(np.mean(left_box[1]))
if len(right_box[1])>50:
right = right_x_low+int(np.mean(right_box[1]))
left_line[0] = np.concatenate(left_line[0])
left_line[1] = np.concatenate(left_line[1])
right_line[0] = np.concatenate(right_line[0])
right_line[1] = np.concatenate(right_line[1])
# Debugger function to highlight the points asigned to lane line
if debug == 1:
plt.plot(left_line[0],left_line[1],'.',color='brown')
plt.plot(right_line[0],right_line[1],'.',color='brown')
# polyfit
# if there are left/right lane lines points, calculate and return
# polynoms coef-s, else return None
if (len(left_line[1]>0)&len(right_line[1])>0):
left_fit=np.polyfit(left_line[1],left_line[0],2)
right_fit=np.polyfit(right_line[1],right_line[0],2)
return left_fit, right_fit
else:
return None, None
In [704]:
def get_line(lines_persp, fit):
"""
Function gets warped binary image and polynom's coef-s of one line
and returns 1 channel image with the line
"""
binary_l = np.zeros_like(lines_persp)
ploty = np.linspace(0, lines_persp.shape[0]-1, lines_persp.shape[0])
plotx = fit[0]*ploty**2 + fit[1]*ploty + fit[2]
line_points = np.column_stack((plotx,ploty))
line = cv2.polylines(binary_l, np.int32([line_points]),color=255,isClosed=0,thickness=20)
return line
In [616]:
def fill_lane(lines_persp):
"""
Alternative function to get_line. This function takes binary warped image,
calls first_lines for polynoms coef-s and returns the filled polygon between
2 lane lines
"""
left_fit, right_fit = first_lines(lines_persp)
if (left_fit != None and right_fit != None):
binary_l = np.zeros_like(lines_persp)
ploty = np.linspace(0, lines_persp.shape[0]-1, lines_persp.shape[0])
plotx_l = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
plotx_r = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
line_points_l = np.column_stack((plotx_l,ploty))
line_points_r = np.column_stack((plotx_r,ploty))
line_points = np.concatenate((line_points_l,line_points_r[::-1],line_points_l[:1]))
cv2.fillPoly(binary_l, np.int32([line_points]),color=255)
result = np.dstack((np.zeros_like(lines_persp),binary_l,np.zeros_like(lines_persp))).astype('uint8')
return result
else:
zero = np.zeros_like(lines_persp)
return np.dstack((zero,zero,zero)).astype('uint8')
poly = fill_lane(lines_persp)
plt.imshow(poly, cmap='gray')
Out[616]:
<matplotlib.image.AxesImage at 0x252a256d8>
In [705]:
def weighted_img(img, initial_img, Ξ±=0.8, Ξ²=1., Ξ»=0.):
"""
Function takes 2 images as well as coef-s and adds second
image on the first one. Returns result image
initial_img * Ξ± + img * Ξ² + Ξ»
"""
return cv2.addWeighted(initial_img, Ξ±, img, Ξ², Ξ»)
In [541]:
def draw_lines(lines_persp, left_fit, right_fit):
"""
Support function to plot lines on the warped image. Is not used for
production
"""
ploty = np.linspace(0, lines_persp.shape[0]-1, lines_persp.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
plt.imshow(lines_persp,cmap='gray')
plt.plot(left_fitx, ploty, color=(0,1,0),linewidth=10)
plt.plot(right_fitx, ploty, color=(0,0,1),linewidth=10)
plt.xlim(0, lines_persp.shape[1])
plt.ylim(lines_persp.shape[0], 0)
histogram = hist(lines_persp)
mid_point = int(len(histogram)/2)
left = int(np.argmax(histogram[:mid_point]))
right = int(np.argmax(histogram[mid_point:])) + mid_point
plt.plot(lines_persp.shape[0]-histogram, linewidth=1, color = 'green')
plt.plot(left, lines_persp.shape[0]-10, '*', color = 'yellow')
plt.plot(right, lines_persp.shape[0]-10, '*', color = 'yellow')
Below I'm exploring the pipeline of lane lines detection and ploting on the given image. On the next step the pipeline will be transformed into function
In [706]:
#0: load image
fname = '/Users/olegair/Documents/MyNewCareer/SDCND/p4/CarND-Advanced-Lane-Lines/test_images/test5.jpg'
img_bgr = cv2.imread(fname)
img = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
#1: Distortion Correction
dst = undist(img)
show_2gr(img,dst)
#2: Get binary image
sxbinary = abs_sobel_thresh(dst, orient='x', thresh_min=20, thresh_max=100)
hls = cv2.cvtColor(dst, cv2.COLOR_RGB2HLS)
s_th = ch_threshold(hls, 2, thresh=(100,150))
s_sx = binary_or(sxbinary, s_th)
show_2gr(sxbinary, s_th)
#3: Warp binary image
lines_persp = warp(s_sx)
show_2gr(s_sx,lines_persp)
#draw_lines(lines_persp,left_fit, right_fit)
#4: Find lines polynoms
left_fit, right_fit = first_lines(lines_persp)
#5: Create warped image with lines
left_right_lines=fill_lane(lines_persp)
#6: Unwarped lines image
lines_unwarp = unwarp(left_right_lines)
#7: Merge initial image with unwarped lines image
img_lines = weighted_img(dst, lines_unwarp, Ξ±=1, Ξ²=0.9, Ξ»=0.)
show_2gr(lines_unwarp,img_lines)
In [707]:
def pipeline(img):
"""
Function takes an RGB image, applied distortion correction,
detects lane lines, draws polygon between lines, add it on
the initial image and returns the resulted image
"""
#1: Distortion Correction
dst = undist(img)
#2: Get binary image
sxbinary = abs_sobel_thresh(dst, orient='x', thresh_min=20, thresh_max=100)
hls = cv2.cvtColor(dst, cv2.COLOR_RGB2HLS)
s_th = ch_threshold(hls, 2, thresh=(170,255))
s_sx = binary_or(sxbinary, s_th)
#3: Warp binary image
lines_persp = warp(s_sx)
#4: Find lines polynoms
left_fit, right_fit = first_lines(lines_persp)
#5: Create warped image with lines
left_right_lines=fill_lane(lines_persp)
#6: Unwarped lines image
lines_unwarp = unwarp(left_right_lines)
#7: Merge initial image with unwarped lines image
img_lines = weighted_img(dst, lines_unwarp, Ξ±=0.5, Ξ²=1, Ξ»=0.)
return img_lines
Lane lines detection on tested images¶
Below I'm detecting lane lines on the test images, provided by Udacity
In [641]:
url = '../CarND-Advanced-Lane-Lines/test_images/'
name = 'test*.jpg'
f, ax = plt.subplots(3,2, figsize=(20,20))
images = glob.glob(url+name)
for i,fname in enumerate(images):
img_bgr = cv2.imread(fname)
img = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
img_lines = pipeline(img)
ax[i%3,i%2].imshow(img_lines)
ax[i%3,i%2].set_title(fname)
On the next step I'm applying my lane lines detection algorithm to 3 videos, provided by Udacity. Outcome videos are saved on a local disk and will be loaded to GitHub repository.
In [642]:
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
from moviepy.editor import VideoFileClip
clip1= VideoFileClip("../CarND-Advanced-Lane-Lines/project_video.mp4")
white_output = 'project_video_out.mp4'
white_clip = clip1.fl_image(pipeline)
%time white_clip.write_videofile(white_output, audio=False)
[MoviePy] >>>> Building video project_video_out.mp4 [MoviePy] Writing video project_video_out.mp4
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| 270/1261 [00:55<03:47, 4.36it/s] 21%|βββ | 271/1261 [00:56<03:46, 4.37it/s] 22%|βββ | 272/1261 [00:56<03:49, 4.30it/s] 22%|βββ | 273/1261 [00:56<03:43, 4.42it/s] 22%|βββ | 274/1261 [00:56<03:34, 4.60it/s] 22%|βββ | 275/1261 [00:57<03:28, 4.72it/s] 22%|βββ | 276/1261 [00:57<03:33, 4.62it/s] 22%|βββ | 277/1261 [00:57<03:42, 4.43it/s] 22%|βββ | 278/1261 [00:57<03:36, 4.54it/s] 22%|βββ | 279/1261 [00:57<03:35, 4.55it/s] 22%|βββ | 280/1261 [00:58<03:38, 4.49it/s] 22%|βββ | 281/1261 [00:58<03:36, 4.53it/s] 22%|βββ | 282/1261 [00:58<03:31, 4.63it/s] 22%|βββ | 283/1261 [00:58<03:36, 4.52it/s] 23%|βββ | 284/1261 [00:59<03:56, 4.13it/s] 23%|βββ | 285/1261 [00:59<03:49, 4.24it/s] 23%|βββ | 286/1261 [00:59<03:37, 4.48it/s] 23%|βββ | 287/1261 [00:59<03:29, 4.65it/s] 23%|βββ | 288/1261 [00:59<03:41, 4.38it/s] 23%|βββ | 289/1261 [01:00<03:37, 4.48it/s] 23%|βββ | 290/1261 [01:00<03:32, 4.56it/s] 23%|βββ | 291/1261 [01:00<03:27, 4.68it/s] 23%|βββ | 292/1261 [01:00<03:31, 4.58it/s] 23%|βββ | 293/1261 [01:01<03:52, 4.16it/s] 23%|βββ | 294/1261 [01:01<03:44, 4.31it/s] 23%|βββ | 295/1261 [01:01<03:35, 4.47it/s] 23%|βββ | 296/1261 [01:01<03:44, 4.29it/s] 24%|βββ | 297/1261 [01:02<03:51, 4.16it/s] 24%|βββ | 298/1261 [01:02<03:44, 4.29it/s] 24%|βββ | 299/1261 [01:02<03:35, 4.47it/s] 24%|βββ | 300/1261 [01:02<03:27, 4.63it/s] 24%|βββ | 301/1261 [01:02<03:25, 4.68it/s] 24%|βββ | 302/1261 [01:03<03:28, 4.60it/s] 24%|βββ | 303/1261 [01:03<03:25, 4.66it/s] 24%|βββ | 304/1261 [01:03<03:22, 4.73it/s] 24%|βββ | 305/1261 [01:03<03:19, 4.80it/s] 24%|βββ | 306/1261 [01:03<03:26, 4.62it/s] 24%|βββ | 307/1261 [01:04<03:22, 4.70it/s] 24%|βββ | 308/1261 [01:04<03:19, 4.78it/s] 25%|βββ | 309/1261 [01:04<03:18, 4.80it/s] 25%|βββ | 310/1261 [01:04<03:22, 4.69it/s] 25%|βββ | 311/1261 [01:05<03:24, 4.65it/s] 25%|βββ | 312/1261 [01:05<03:28, 4.56it/s] 25%|βββ | 313/1261 [01:05<03:24, 4.63it/s] 25%|βββ | 314/1261 [01:05<03:40, 4.30it/s] 25%|βββ | 315/1261 [01:06<04:26, 3.55it/s] 25%|βββ | 316/1261 [01:06<04:22, 3.60it/s] 25%|βββ | 317/1261 [01:06<04:05, 3.84it/s] 25%|βββ | 318/1261 [01:06<04:07, 3.81it/s] 25%|βββ | 319/1261 [01:07<03:58, 3.94it/s] 25%|βββ | 320/1261 [01:07<04:43, 3.32it/s] 25%|βββ | 321/1261 [01:07<05:03, 3.10it/s] 26%|βββ | 322/1261 [01:08<05:13, 3.00it/s] 26%|βββ | 323/1261 [01:08<05:04, 3.08it/s] 26%|βββ | 324/1261 [01:08<04:44, 3.29it/s] 26%|βββ | 325/1261 [01:09<04:31, 3.44it/s] 26%|βββ | 326/1261 [01:09<04:20, 3.59it/s] 26%|βββ | 327/1261 [01:09<04:00, 3.89it/s] 26%|βββ | 328/1261 [01:09<03:46, 4.12it/s] 26%|βββ | 329/1261 [01:09<03:34, 4.34it/s] 26%|βββ | 330/1261 [01:10<03:36, 4.30it/s] 26%|βββ | 331/1261 [01:10<03:34, 4.35it/s] 26%|βββ | 332/1261 [01:10<03:24, 4.55it/s] 26%|βββ | 333/1261 [01:10<03:22, 4.59it/s] 26%|βββ | 334/1261 [01:11<03:27, 4.46it/s] 27%|βββ | 335/1261 [01:11<03:23, 4.54it/s] 27%|βββ | 336/1261 [01:11<03:16, 4.71it/s] 27%|βββ | 337/1261 [01:11<03:15, 4.73it/s] 27%|βββ | 338/1261 [01:11<03:23, 4.54it/s] 27%|βββ | 339/1261 [01:12<03:17, 4.66it/s] 27%|βββ | 340/1261 [01:12<03:12, 4.79it/s] 27%|βββ | 341/1261 [01:12<03:09, 4.86it/s] 27%|βββ | 342/1261 [01:12<03:11, 4.80it/s] 27%|βββ | 343/1261 [01:12<03:10, 4.83it/s] 27%|βββ | 344/1261 [01:13<03:06, 4.91it/s] 27%|βββ | 345/1261 [01:13<03:05, 4.94it/s] 27%|βββ | 346/1261 [01:13<03:08, 4.86it/s] 28%|βββ | 347/1261 [01:13<03:06, 4.90it/s] 28%|βββ | 348/1261 [01:13<03:06, 4.88it/s] 28%|βββ | 349/1261 [01:14<03:08, 4.83it/s] 28%|βββ | 350/1261 [01:14<03:13, 4.70it/s] 28%|βββ | 351/1261 [01:14<03:12, 4.72it/s] 28%|βββ | 352/1261 [01:14<03:07, 4.84it/s] 28%|βββ | 353/1261 [01:14<03:06, 4.87it/s] 28%|βββ | 354/1261 [01:15<03:11, 4.73it/s] 28%|βββ | 355/1261 [01:15<03:09, 4.77it/s] 28%|βββ | 356/1261 [01:15<03:06, 4.85it/s] 28%|βββ | 357/1261 [01:15<03:03, 4.93it/s] 28%|βββ | 358/1261 [01:16<03:12, 4.70it/s] 28%|βββ | 359/1261 [01:16<03:12, 4.68it/s] 29%|βββ | 360/1261 [01:16<03:00, 4.99it/s] 29%|βββ | 361/1261 [01:16<02:58, 5.03it/s] 29%|βββ | 362/1261 [01:16<03:06, 4.83it/s] 29%|βββ | 363/1261 [01:17<03:05, 4.83it/s] 29%|βββ | 364/1261 [01:17<03:10, 4.71it/s] 29%|βββ | 365/1261 [01:17<03:14, 4.60it/s] 29%|βββ | 366/1261 [01:17<03:26, 4.34it/s] 29%|βββ | 367/1261 [01:17<03:22, 4.43it/s] 29%|βββ | 368/1261 [01:18<03:14, 4.59it/s] 29%|βββ | 369/1261 [01:18<03:16, 4.55it/s] 29%|βββ | 370/1261 [01:18<03:32, 4.20it/s] 29%|βββ | 371/1261 [01:18<03:23, 4.37it/s] 30%|βββ | 372/1261 [01:19<03:23, 4.37it/s] 30%|βββ | 373/1261 [01:19<03:17, 4.50it/s] 30%|βββ | 374/1261 [01:19<03:18, 4.47it/s] 30%|βββ | 375/1261 [01:19<03:15, 4.54it/s] 30%|βββ | 376/1261 [01:19<03:07, 4.72it/s] 30%|βββ | 377/1261 [01:20<03:09, 4.67it/s] 30%|βββ | 378/1261 [01:20<03:16, 4.48it/s] 30%|βββ | 379/1261 [01:20<03:11, 4.59it/s] 30%|βββ | 380/1261 [01:20<03:12, 4.57it/s] 30%|βββ | 381/1261 [01:21<03:10, 4.63it/s] 30%|βββ | 382/1261 [01:21<03:14, 4.52it/s] 30%|βββ | 383/1261 [01:21<03:09, 4.63it/s] 30%|βββ | 384/1261 [01:21<03:07, 4.67it/s] 31%|βββ | 385/1261 [01:21<03:10, 4.61it/s] 31%|βββ | 386/1261 [01:22<03:13, 4.51it/s] 31%|βββ | 387/1261 [01:22<03:09, 4.61it/s] 31%|βββ | 388/1261 [01:22<03:11, 4.56it/s] 31%|βββ | 389/1261 [01:22<03:11, 4.55it/s] 31%|βββ | 390/1261 [01:23<03:22, 4.30it/s] 31%|βββ | 391/1261 [01:23<03:15, 4.44it/s] 31%|βββ | 392/1261 [01:23<03:07, 4.64it/s] 31%|βββ | 393/1261 [01:23<03:02, 4.77it/s] 31%|βββ | 394/1261 [01:23<03:07, 4.62it/s] 31%|ββββ | 395/1261 [01:24<03:06, 4.64it/s] 31%|ββββ | 396/1261 [01:24<03:02, 4.74it/s] 31%|ββββ | 397/1261 [01:24<02:58, 4.85it/s] 32%|ββββ | 398/1261 [01:24<03:20, 4.31it/s] 32%|ββββ | 399/1261 [01:25<03:33, 4.05it/s] 32%|ββββ | 400/1261 [01:25<03:23, 4.22it/s] 32%|ββββ | 401/1261 [01:25<03:20, 4.28it/s] 32%|ββββ | 402/1261 [01:25<03:33, 4.03it/s] 32%|ββββ | 403/1261 [01:26<03:37, 3.95it/s] 32%|ββββ | 404/1261 [01:26<03:23, 4.21it/s] 32%|ββββ | 405/1261 [01:26<03:18, 4.32it/s] 32%|ββββ | 406/1261 [01:26<03:24, 4.17it/s] 32%|ββββ | 407/1261 [01:26<03:22, 4.22it/s] 32%|ββββ | 408/1261 [01:27<03:16, 4.34it/s] 32%|ββββ | 409/1261 [01:27<03:20, 4.25it/s] 33%|ββββ | 410/1261 [01:27<03:22, 4.21it/s] 33%|ββββ | 411/1261 [01:27<03:19, 4.27it/s] 33%|ββββ | 412/1261 [01:28<03:11, 4.43it/s] 33%|ββββ | 413/1261 [01:28<03:09, 4.47it/s] 33%|ββββ | 414/1261 [01:28<03:20, 4.23it/s] 33%|ββββ | 415/1261 [01:28<03:18, 4.26it/s] 33%|ββββ | 416/1261 [01:29<03:11, 4.42it/s] 33%|ββββ | 417/1261 [01:29<03:05, 4.55it/s] 33%|ββββ | 418/1261 [01:29<03:08, 4.47it/s] 33%|ββββ | 419/1261 [01:29<03:10, 4.41it/s] 33%|ββββ | 420/1261 [01:29<03:08, 4.47it/s] 33%|ββββ | 421/1261 [01:30<03:04, 4.56it/s] 33%|ββββ | 422/1261 [01:30<03:07, 4.48it/s] 34%|ββββ | 423/1261 [01:30<03:03, 4.56it/s] 34%|ββββ | 424/1261 [01:30<02:58, 4.70it/s] 34%|ββββ | 425/1261 [01:30<02:53, 4.82it/s] 34%|ββββ | 426/1261 [01:31<03:07, 4.46it/s] 34%|ββββ | 427/1261 [01:31<03:07, 4.45it/s] 34%|ββββ | 428/1261 [01:31<03:06, 4.47it/s] 34%|ββββ | 429/1261 [01:31<03:06, 4.47it/s] 34%|ββββ | 430/1261 [01:32<03:13, 4.30it/s] 34%|ββββ | 431/1261 [01:32<03:12, 4.32it/s] 34%|ββββ | 432/1261 [01:32<03:08, 4.40it/s] 34%|ββββ | 433/1261 [01:32<03:01, 4.57it/s] 34%|ββββ | 434/1261 [01:33<03:03, 4.51it/s] 34%|ββββ | 435/1261 [01:33<03:03, 4.49it/s] 35%|ββββ | 436/1261 [01:33<02:57, 4.64it/s] 35%|ββββ | 437/1261 [01:33<02:55, 4.69it/s] 35%|ββββ | 438/1261 [01:33<02:57, 4.64it/s] 35%|ββββ | 439/1261 [01:34<02:57, 4.62it/s] 35%|ββββ | 440/1261 [01:34<02:53, 4.72it/s] 35%|ββββ | 441/1261 [01:34<02:52, 4.76it/s] 35%|ββββ | 442/1261 [01:34<02:57, 4.62it/s] 35%|ββββ | 443/1261 [01:34<02:56, 4.64it/s] 35%|ββββ | 444/1261 [01:35<02:54, 4.69it/s] 35%|ββββ | 445/1261 [01:35<02:50, 4.78it/s] 35%|ββββ | 446/1261 [01:35<02:54, 4.68it/s] 35%|ββββ | 447/1261 [01:35<02:50, 4.76it/s] 36%|ββββ | 448/1261 [01:36<02:52, 4.71it/s] 36%|ββββ | 449/1261 [01:36<02:53, 4.68it/s] 36%|ββββ | 450/1261 [01:36<03:00, 4.49it/s] 36%|ββββ | 451/1261 [01:36<02:56, 4.59it/s] 36%|ββββ | 452/1261 [01:36<02:53, 4.65it/s] 36%|ββββ | 453/1261 [01:37<02:49, 4.77it/s] 36%|ββββ | 454/1261 [01:37<02:54, 4.63it/s] 36%|ββββ | 455/1261 [01:37<02:53, 4.65it/s] 36%|ββββ | 456/1261 [01:37<02:50, 4.71it/s] 36%|ββββ | 457/1261 [01:37<02:48, 4.77it/s] 36%|ββββ | 458/1261 [01:38<02:53, 4.63it/s] 36%|ββββ | 459/1261 [01:38<02:50, 4.69it/s] 36%|ββββ | 460/1261 [01:38<02:50, 4.70it/s] 37%|ββββ | 461/1261 [01:38<02:46, 4.79it/s] 37%|ββββ | 462/1261 [01:39<02:51, 4.66it/s] 37%|ββββ | 463/1261 [01:39<02:49, 4.72it/s] 37%|ββββ | 464/1261 [01:39<02:46, 4.79it/s] 37%|ββββ | 465/1261 [01:39<02:44, 4.85it/s] 37%|ββββ | 466/1261 [01:39<02:49, 4.69it/s] 37%|ββββ | 467/1261 [01:40<02:49, 4.69it/s] 37%|ββββ | 468/1261 [01:40<02:46, 4.76it/s] 37%|ββββ | 469/1261 [01:40<02:47, 4.74it/s] 37%|ββββ | 470/1261 [01:40<02:53, 4.56it/s] 37%|ββββ | 471/1261 [01:40<02:54, 4.53it/s] 37%|ββββ | 472/1261 [01:41<02:51, 4.60it/s] 38%|ββββ | 473/1261 [01:41<02:50, 4.62it/s] 38%|ββββ | 474/1261 [01:41<02:52, 4.56it/s] 38%|ββββ | 475/1261 [01:41<02:49, 4.63it/s] 38%|ββββ | 476/1261 [01:42<02:46, 4.70it/s] 38%|ββββ | 477/1261 [01:42<02:36, 5.02it/s] 38%|ββββ | 478/1261 [01:42<02:41, 4.86it/s] 38%|ββββ | 479/1261 [01:42<02:43, 4.79it/s] 38%|ββββ | 480/1261 [01:42<02:41, 4.84it/s] 38%|ββββ | 481/1261 [01:42<02:34, 5.06it/s] 38%|ββββ | 482/1261 [01:43<02:41, 4.81it/s] 38%|ββββ | 483/1261 [01:43<02:43, 4.75it/s] 38%|ββββ | 484/1261 [01:43<02:43, 4.75it/s] 38%|ββββ | 485/1261 [01:43<02:42, 4.78it/s] 39%|ββββ | 486/1261 [01:44<02:50, 4.55it/s] 39%|ββββ | 487/1261 [01:44<02:48, 4.60it/s] 39%|ββββ | 488/1261 [01:44<02:44, 4.70it/s] 39%|ββββ | 489/1261 [01:44<02:42, 4.75it/s] 39%|ββββ | 490/1261 [01:44<02:50, 4.53it/s] 39%|ββββ | 491/1261 [01:45<02:42, 4.75it/s] 39%|ββββ | 492/1261 [01:45<02:43, 4.70it/s] 39%|ββββ | 493/1261 [01:45<02:40, 4.80it/s] 39%|ββββ | 494/1261 [01:45<02:46, 4.61it/s] 39%|ββββ | 495/1261 [01:46<02:44, 4.64it/s] 39%|ββββ | 496/1261 [01:46<02:43, 4.68it/s] 39%|ββββ | 497/1261 [01:46<02:41, 4.72it/s] 39%|ββββ | 498/1261 [01:46<02:45, 4.61it/s] 40%|ββββ | 499/1261 [01:46<02:46, 4.58it/s] 40%|ββββ | 500/1261 [01:47<02:44, 4.63it/s] 40%|ββββ | 501/1261 [01:47<02:41, 4.70it/s] 40%|ββββ | 502/1261 [01:47<02:49, 4.49it/s] 40%|ββββ | 503/1261 [01:47<02:45, 4.57it/s] 40%|ββββ | 504/1261 [01:47<02:42, 4.66it/s] 40%|ββββ | 505/1261 [01:48<02:38, 4.76it/s] 40%|ββββ | 506/1261 [01:48<02:45, 4.55it/s] 40%|ββββ | 507/1261 [01:48<02:45, 4.57it/s] 40%|ββββ | 508/1261 [01:48<02:41, 4.66it/s] 40%|ββββ | 509/1261 [01:49<02:39, 4.72it/s] 40%|ββββ | 510/1261 [01:49<02:45, 4.54it/s] 41%|ββββ | 511/1261 [01:49<02:47, 4.48it/s] 41%|ββββ | 512/1261 [01:49<02:43, 4.58it/s] 41%|ββββ | 513/1261 [01:49<02:42, 4.61it/s] 41%|ββββ | 514/1261 [01:50<02:43, 4.56it/s] 41%|ββββ | 515/1261 [01:50<02:41, 4.63it/s] 41%|ββββ | 516/1261 [01:50<02:38, 4.69it/s] 41%|ββββ | 517/1261 [01:50<02:36, 4.75it/s] 41%|ββββ | 518/1261 [01:50<02:32, 4.86it/s] 41%|ββββ | 519/1261 [01:51<02:34, 4.81it/s] 41%|ββββ | 520/1261 [01:51<02:37, 4.72it/s] 41%|βββββ | 521/1261 [01:51<02:35, 4.76it/s] 41%|βββββ | 522/1261 [01:51<02:56, 4.20it/s] 41%|βββββ | 523/1261 [01:52<03:06, 3.96it/s] 42%|βββββ | 524/1261 [01:52<03:09, 3.88it/s] 42%|βββββ | 525/1261 [01:52<03:13, 3.80it/s] 42%|βββββ | 526/1261 [01:53<03:41, 3.32it/s] 42%|βββββ | 527/1261 [01:53<03:39, 3.34it/s] 42%|βββββ | 528/1261 [01:53<03:24, 3.59it/s] 42%|βββββ | 529/1261 [01:53<03:06, 3.93it/s] 42%|βββββ | 530/1261 [01:54<03:03, 3.98it/s] 42%|βββββ | 531/1261 [01:54<02:56, 4.13it/s] 42%|βββββ | 532/1261 [01:54<02:47, 4.34it/s] 42%|βββββ | 533/1261 [01:54<02:40, 4.54it/s] 42%|βββββ | 534/1261 [01:54<02:40, 4.52it/s] 42%|βββββ | 535/1261 [01:55<02:37, 4.62it/s] 43%|βββββ | 536/1261 [01:55<02:34, 4.69it/s] 43%|βββββ | 537/1261 [01:55<02:33, 4.73it/s] 43%|βββββ | 538/1261 [01:55<02:35, 4.66it/s] 43%|βββββ | 539/1261 [01:55<02:35, 4.65it/s] 43%|βββββ | 540/1261 [01:56<02:31, 4.76it/s] 43%|βββββ | 541/1261 [01:56<02:31, 4.74it/s] 43%|βββββ | 542/1261 [01:56<02:34, 4.64it/s] 43%|βββββ | 543/1261 [01:56<02:31, 4.75it/s] 43%|βββββ | 544/1261 [01:57<02:31, 4.73it/s] 43%|βββββ | 545/1261 [01:57<02:26, 4.87it/s] 43%|βββββ | 546/1261 [01:57<02:23, 4.97it/s] 43%|βββββ | 547/1261 [01:57<02:20, 5.07it/s] 43%|βββββ | 548/1261 [01:57<02:23, 4.98it/s] 44%|βββββ | 549/1261 [01:58<02:25, 4.91it/s] 44%|βββββ | 550/1261 [01:58<02:26, 4.85it/s] 44%|βββββ | 551/1261 [01:58<02:20, 5.05it/s] 44%|βββββ | 552/1261 [01:58<02:25, 4.86it/s] 44%|βββββ | 553/1261 [01:58<02:24, 4.89it/s] 44%|βββββ | 554/1261 [01:59<02:20, 5.03it/s] 44%|βββββ | 555/1261 [01:59<02:16, 5.16it/s] 44%|βββββ | 556/1261 [01:59<02:23, 4.90it/s] 44%|βββββ | 557/1261 [01:59<02:15, 5.19it/s] 44%|βββββ | 558/1261 [01:59<02:14, 5.24it/s] 44%|βββββ | 559/1261 [01:59<02:15, 5.17it/s] 44%|βββββ | 560/1261 [02:00<02:23, 4.90it/s] 44%|βββββ | 561/1261 [02:00<02:27, 4.76it/s] 45%|βββββ | 562/1261 [02:00<02:26, 4.78it/s] 45%|βββββ | 563/1261 [02:00<02:27, 4.73it/s] 45%|βββββ | 564/1261 [02:01<02:28, 4.70it/s] 45%|βββββ | 565/1261 [02:01<02:20, 4.95it/s] 45%|βββββ | 566/1261 [02:01<02:12, 5.26it/s] 45%|βββββ | 567/1261 [02:01<02:08, 5.41it/s] 45%|βββββ | 568/1261 [02:01<02:08, 5.38it/s] 45%|βββββ | 569/1261 [02:01<02:13, 5.17it/s] 45%|βββββ | 570/1261 [02:02<02:07, 5.41it/s] 45%|βββββ | 571/1261 [02:02<02:04, 5.55it/s] 45%|βββββ | 572/1261 [02:02<02:08, 5.36it/s] 45%|βββββ | 573/1261 [02:02<02:08, 5.34it/s] 46%|βββββ | 574/1261 [02:02<02:08, 5.36it/s] 46%|βββββ | 575/1261 [02:03<02:02, 5.61it/s] 46%|βββββ | 576/1261 [02:03<02:03, 5.54it/s] 46%|βββββ | 577/1261 [02:03<01:59, 5.74it/s] 46%|βββββ | 578/1261 [02:03<01:54, 5.96it/s] 46%|βββββ | 579/1261 [02:03<01:55, 5.91it/s] 46%|βββββ | 580/1261 [02:03<01:59, 5.68it/s] 46%|βββββ | 581/1261 [02:04<01:58, 5.73it/s] 46%|βββββ | 582/1261 [02:04<02:04, 5.45it/s] 46%|βββββ | 583/1261 [02:04<02:11, 5.15it/s] 46%|βββββ | 584/1261 [02:04<02:28, 4.55it/s] 46%|βββββ | 585/1261 [02:04<02:18, 4.89it/s] 46%|βββββ | 586/1261 [02:05<02:09, 5.20it/s] 47%|βββββ | 587/1261 [02:05<02:06, 5.34it/s] 47%|βββββ | 588/1261 [02:05<02:02, 5.51it/s] 47%|βββββ | 589/1261 [02:05<01:57, 5.73it/s] 47%|βββββ | 590/1261 [02:05<01:54, 5.84it/s] 47%|βββββ | 591/1261 [02:05<01:57, 5.71it/s] 47%|βββββ | 592/1261 [02:06<02:05, 5.34it/s] 47%|βββββ | 593/1261 [02:06<01:59, 5.59it/s] 47%|βββββ | 594/1261 [02:06<01:53, 5.87it/s] 47%|βββββ | 595/1261 [02:06<01:50, 6.02it/s] 47%|βββββ | 596/1261 [02:06<01:53, 5.84it/s] 47%|βββββ | 597/1261 [02:07<01:51, 5.94it/s] 47%|βββββ | 598/1261 [02:07<01:53, 5.84it/s] 48%|βββββ | 599/1261 [02:07<02:00, 5.49it/s] 48%|βββββ | 600/1261 [02:07<02:18, 4.77it/s] 48%|βββββ | 601/1261 [02:07<02:17, 4.80it/s] 48%|βββββ | 602/1261 [02:08<02:15, 4.85it/s] 48%|βββββ | 603/1261 [02:08<02:16, 4.83it/s] 48%|βββββ | 604/1261 [02:08<02:21, 4.63it/s] 48%|βββββ | 605/1261 [02:08<02:21, 4.65it/s] 48%|βββββ | 606/1261 [02:08<02:12, 4.94it/s] 48%|βββββ | 607/1261 [02:09<02:05, 5.23it/s] 48%|βββββ | 608/1261 [02:09<02:15, 4.82it/s] 48%|βββββ | 609/1261 [02:09<02:22, 4.59it/s] 48%|βββββ | 610/1261 [02:09<02:30, 4.31it/s] 48%|βββββ | 611/1261 [02:10<02:28, 4.38it/s] 49%|βββββ | 612/1261 [02:10<02:27, 4.39it/s] 49%|βββββ | 613/1261 [02:10<02:27, 4.39it/s] 49%|βββββ | 614/1261 [02:10<02:37, 4.11it/s] 49%|βββββ | 615/1261 [02:11<02:34, 4.19it/s] 49%|βββββ | 616/1261 [02:11<02:30, 4.27it/s] 49%|βββββ | 617/1261 [02:11<02:28, 4.33it/s] 49%|βββββ | 618/1261 [02:11<02:30, 4.28it/s] 49%|βββββ | 619/1261 [02:11<02:30, 4.27it/s] 49%|βββββ | 620/1261 [02:12<02:30, 4.27it/s] 49%|βββββ | 621/1261 [02:12<02:30, 4.25it/s] 49%|βββββ | 622/1261 [02:12<02:33, 4.16it/s] 49%|βββββ | 623/1261 [02:12<02:35, 4.09it/s] 49%|βββββ | 624/1261 [02:13<02:30, 4.22it/s] 50%|βββββ | 625/1261 [02:13<02:27, 4.32it/s] 50%|βββββ | 626/1261 [02:13<02:27, 4.30it/s] 50%|βββββ | 627/1261 [02:13<02:23, 4.41it/s] 50%|βββββ | 628/1261 [02:14<02:21, 4.48it/s] 50%|βββββ | 629/1261 [02:14<02:28, 4.25it/s] 50%|βββββ | 630/1261 [02:14<02:25, 4.35it/s] 50%|βββββ | 631/1261 [02:14<02:21, 4.44it/s] 50%|βββββ | 632/1261 [02:14<02:19, 4.50it/s] 50%|βββββ | 633/1261 [02:15<02:28, 4.23it/s] 50%|βββββ | 634/1261 [02:15<02:27, 4.26it/s] 50%|βββββ | 635/1261 [02:15<02:24, 4.34it/s] 50%|βββββ | 636/1261 [02:15<02:22, 4.40it/s] 51%|βββββ | 637/1261 [02:16<02:30, 4.15it/s] 51%|βββββ | 638/1261 [02:16<02:30, 4.13it/s] 51%|βββββ | 639/1261 [02:16<02:27, 4.22it/s] 51%|βββββ | 640/1261 [02:16<02:24, 4.30it/s] 51%|βββββ | 641/1261 [02:17<02:28, 4.17it/s] 51%|βββββ | 642/1261 [02:17<02:26, 4.23it/s] 51%|βββββ | 643/1261 [02:17<02:21, 4.37it/s] 51%|βββββ | 644/1261 [02:17<02:19, 4.43it/s] 51%|βββββ | 645/1261 [02:18<02:26, 4.21it/s] 51%|βββββ | 646/1261 [02:18<02:29, 4.12it/s] 51%|ββββββ | 647/1261 [02:18<02:25, 4.21it/s] 51%|ββββββ | 648/1261 [02:18<02:28, 4.12it/s] 51%|ββββββ | 649/1261 [02:19<02:36, 3.92it/s] 52%|ββββββ | 650/1261 [02:19<02:35, 3.92it/s] 52%|ββββββ | 651/1261 [02:19<02:31, 4.02it/s] 52%|ββββββ | 652/1261 [02:19<02:27, 4.12it/s] 52%|ββββββ | 653/1261 [02:19<02:28, 4.08it/s] 52%|ββββββ | 654/1261 [02:20<02:25, 4.17it/s] 52%|ββββββ | 655/1261 [02:20<02:21, 4.27it/s] 52%|ββββββ | 656/1261 [02:20<02:18, 4.37it/s] 52%|ββββββ | 657/1261 [02:20<02:29, 4.05it/s] 52%|ββββββ | 658/1261 [02:21<02:32, 3.96it/s] 52%|ββββββ | 659/1261 [02:21<02:22, 4.22it/s] 52%|ββββββ | 660/1261 [02:21<02:18, 4.34it/s] 52%|ββββββ | 661/1261 [02:21<02:25, 4.13it/s] 52%|ββββββ | 662/1261 [02:22<02:15, 4.41it/s] 53%|ββββββ | 663/1261 [02:22<02:12, 4.53it/s] 53%|ββββββ | 664/1261 [02:22<02:09, 4.60it/s] 53%|ββββββ | 665/1261 [02:22<02:22, 4.19it/s] 53%|ββββββ | 666/1261 [02:23<02:23, 4.15it/s] 53%|ββββββ | 667/1261 [02:23<02:24, 4.12it/s] 53%|ββββββ | 668/1261 [02:23<02:19, 4.24it/s] 53%|ββββββ | 669/1261 [02:23<02:22, 4.17it/s] 53%|ββββββ | 670/1261 [02:24<02:26, 4.03it/s] 53%|ββββββ | 671/1261 [02:24<02:23, 4.12it/s] 53%|ββββββ | 672/1261 [02:24<02:19, 4.23it/s] 53%|ββββββ | 673/1261 [02:24<02:22, 4.14it/s] 53%|ββββββ | 674/1261 [02:24<02:19, 4.22it/s] 54%|ββββββ | 675/1261 [02:25<02:14, 4.36it/s] 54%|ββββββ | 676/1261 [02:25<02:15, 4.32it/s] 54%|ββββββ | 677/1261 [02:25<02:19, 4.18it/s] 54%|ββββββ | 678/1261 [02:25<02:16, 4.27it/s] 54%|ββββββ | 679/1261 [02:26<02:15, 4.28it/s] 54%|ββββββ | 680/1261 [02:26<02:16, 4.25it/s] 54%|ββββββ | 681/1261 [02:26<02:22, 4.08it/s] 54%|ββββββ | 682/1261 [02:26<02:18, 4.19it/s] 54%|ββββββ | 683/1261 [02:27<02:13, 4.32it/s] 54%|ββββββ | 684/1261 [02:27<02:09, 4.46it/s] 54%|ββββββ | 685/1261 [02:27<02:14, 4.27it/s] 54%|ββββββ | 686/1261 [02:27<02:16, 4.21it/s] 54%|ββββββ | 687/1261 [02:27<02:11, 4.37it/s] 55%|ββββββ | 688/1261 [02:28<02:08, 4.47it/s] 55%|ββββββ | 689/1261 [02:28<02:10, 4.38it/s] 55%|ββββββ | 690/1261 [02:28<02:10, 4.39it/s] 55%|ββββββ | 691/1261 [02:28<02:06, 4.50it/s] 55%|ββββββ | 692/1261 [02:29<02:02, 4.63it/s] 55%|ββββββ | 693/1261 [02:29<02:05, 4.54it/s] 55%|ββββββ | 694/1261 [02:29<02:02, 4.62it/s] 55%|ββββββ | 695/1261 [02:29<02:02, 4.63it/s] 55%|ββββββ | 696/1261 [02:29<02:02, 4.63it/s] 55%|ββββββ | 697/1261 [02:30<02:04, 4.54it/s] 55%|ββββββ | 698/1261 [02:30<02:01, 4.62it/s] 55%|ββββββ | 699/1261 [02:30<01:59, 4.71it/s] 56%|ββββββ | 700/1261 [02:30<01:57, 4.78it/s] 56%|ββββββ | 701/1261 [02:30<01:59, 4.67it/s] 56%|ββββββ | 702/1261 [02:31<01:59, 4.68it/s] 56%|ββββββ | 703/1261 [02:31<01:57, 4.74it/s] 56%|ββββββ | 704/1261 [02:31<01:55, 4.81it/s] 56%|ββββββ | 705/1261 [02:31<02:00, 4.61it/s] 56%|ββββββ | 706/1261 [02:32<02:02, 4.52it/s] 56%|ββββββ | 707/1261 [02:32<01:59, 4.64it/s] 56%|ββββββ | 708/1261 [02:32<01:56, 4.74it/s] 56%|ββββββ | 709/1261 [02:32<01:58, 4.65it/s] 56%|ββββββ | 710/1261 [02:32<01:56, 4.72it/s] 56%|ββββββ | 711/1261 [02:33<01:53, 4.83it/s] 56%|ββββββ | 712/1261 [02:33<01:57, 4.69it/s] 57%|ββββββ | 713/1261 [02:33<02:00, 4.55it/s] 57%|ββββββ | 714/1261 [02:33<01:59, 4.60it/s] 57%|ββββββ | 715/1261 [02:34<01:57, 4.64it/s] 57%|ββββββ | 716/1261 [02:34<01:56, 4.69it/s] 57%|ββββββ | 717/1261 [02:34<02:01, 4.47it/s] 57%|ββββββ | 718/1261 [02:34<01:58, 4.59it/s] 57%|ββββββ | 719/1261 [02:34<01:49, 4.97it/s] 57%|ββββββ | 720/1261 [02:35<01:49, 4.94it/s] 57%|ββββββ | 721/1261 [02:35<01:52, 4.80it/s] 57%|ββββββ | 722/1261 [02:35<01:51, 4.82it/s] 57%|ββββββ | 723/1261 [02:35<01:50, 4.86it/s] 57%|ββββββ | 724/1261 [02:35<01:43, 5.17it/s] 57%|ββββββ | 725/1261 [02:36<01:49, 4.91it/s] 58%|ββββββ | 726/1261 [02:36<01:50, 4.83it/s] 58%|ββββββ | 727/1261 [02:36<01:52, 4.76it/s] 58%|ββββββ | 728/1261 [02:36<01:51, 4.78it/s] 58%|ββββββ | 729/1261 [02:36<01:54, 4.66it/s] 58%|ββββββ | 730/1261 [02:37<01:53, 4.69it/s] 58%|ββββββ | 731/1261 [02:37<01:51, 4.74it/s] 58%|ββββββ | 732/1261 [02:37<01:51, 4.76it/s] 58%|ββββββ | 733/1261 [02:37<01:54, 4.62it/s] 58%|ββββββ | 734/1261 [02:37<01:52, 4.67it/s] 58%|ββββββ | 735/1261 [02:38<01:51, 4.73it/s] 58%|ββββββ | 736/1261 [02:38<01:49, 4.77it/s] 58%|ββββββ | 737/1261 [02:38<01:54, 4.56it/s] 59%|ββββββ | 738/1261 [02:38<01:55, 4.52it/s] 59%|ββββββ | 739/1261 [02:39<01:53, 4.60it/s] 59%|ββββββ | 740/1261 [02:39<01:51, 4.68it/s] 59%|ββββββ | 741/1261 [02:39<01:53, 4.56it/s] 59%|ββββββ | 742/1261 [02:39<01:52, 4.61it/s] 59%|ββββββ | 743/1261 [02:39<01:50, 4.69it/s] 59%|ββββββ | 744/1261 [02:40<01:48, 4.77it/s] 59%|ββββββ | 745/1261 [02:40<01:50, 4.65it/s] 59%|ββββββ | 746/1261 [02:40<01:50, 4.67it/s] 59%|ββββββ | 747/1261 [02:40<01:47, 4.76it/s] 59%|ββββββ | 748/1261 [02:40<01:48, 4.71it/s] 59%|ββββββ | 749/1261 [02:41<01:50, 4.62it/s] 59%|ββββββ | 750/1261 [02:41<01:49, 4.65it/s] 60%|ββββββ | 751/1261 [02:41<01:48, 4.71it/s] 60%|ββββββ | 752/1261 [02:41<01:46, 4.77it/s] 60%|ββββββ | 753/1261 [02:42<01:49, 4.63it/s] 60%|ββββββ | 754/1261 [02:42<01:47, 4.73it/s] 60%|ββββββ | 755/1261 [02:42<01:46, 4.77it/s] 60%|ββββββ | 756/1261 [02:42<01:43, 4.86it/s] 60%|ββββββ | 757/1261 [02:42<01:48, 4.66it/s] 60%|ββββββ | 758/1261 [02:43<01:49, 4.60it/s] 60%|ββββββ | 759/1261 [02:43<01:48, 4.61it/s] 60%|ββββββ | 760/1261 [02:43<01:46, 4.72it/s] 60%|ββββββ | 761/1261 [02:43<01:47, 4.65it/s] 60%|ββββββ | 762/1261 [02:43<01:47, 4.63it/s] 61%|ββββββ | 763/1261 [02:44<01:45, 4.72it/s] 61%|ββββββ | 764/1261 [02:44<01:44, 4.75it/s] 61%|ββββββ | 765/1261 [02:44<01:46, 4.66it/s] 61%|ββββββ | 766/1261 [02:44<01:45, 4.69it/s] 61%|ββββββ | 767/1261 [02:45<01:44, 4.71it/s] 61%|ββββββ | 768/1261 [02:45<01:42, 4.79it/s] 61%|ββββββ | 769/1261 [02:45<01:50, 4.47it/s] 61%|ββββββ | 770/1261 [02:45<01:48, 4.54it/s] 61%|ββββββ | 771/1261 [02:45<01:45, 4.62it/s] 61%|ββββββ | 772/1261 [02:46<01:43, 4.70it/s] 61%|βββββββ | 773/1261 [02:46<01:46, 4.58it/s] 61%|βββββββ | 774/1261 [02:46<01:45, 4.60it/s] 61%|βββββββ | 775/1261 [02:46<01:44, 4.64it/s] 62%|βββββββ | 776/1261 [02:46<01:42, 4.72it/s] 62%|βββββββ | 777/1261 [02:47<01:45, 4.60it/s] 62%|βββββββ | 778/1261 [02:47<01:45, 4.59it/s] 62%|βββββββ | 779/1261 [02:47<01:46, 4.55it/s] 62%|βββββββ | 780/1261 [02:47<01:44, 4.60it/s] 62%|βββββββ | 781/1261 [02:48<01:46, 4.51it/s] 62%|βββββββ | 782/1261 [02:48<01:44, 4.59it/s] 62%|βββββββ | 783/1261 [02:48<01:41, 4.71it/s] 62%|βββββββ | 784/1261 [02:48<01:39, 4.81it/s] 62%|βββββββ | 785/1261 [02:48<01:42, 4.62it/s] 62%|βββββββ | 786/1261 [02:49<01:43, 4.61it/s] 62%|βββββββ | 787/1261 [02:49<01:41, 4.67it/s] 62%|βββββββ | 788/1261 [02:49<01:40, 4.72it/s] 63%|βββββββ | 789/1261 [02:49<01:43, 4.55it/s] 63%|βββββββ | 790/1261 [02:50<01:43, 4.56it/s] 63%|βββββββ | 791/1261 [02:50<01:39, 4.72it/s] 63%|βββββββ | 792/1261 [02:50<01:37, 4.82it/s] 63%|βββββββ | 793/1261 [02:50<01:40, 4.66it/s] 63%|βββββββ | 794/1261 [02:50<01:44, 4.49it/s] 63%|βββββββ | 795/1261 [02:51<01:41, 4.60it/s] 63%|βββββββ | 796/1261 [02:51<01:39, 4.67it/s] 63%|βββββββ | 797/1261 [02:51<01:36, 4.80it/s] 63%|βββββββ | 798/1261 [02:51<01:43, 4.46it/s] 63%|βββββββ | 799/1261 [02:52<01:52, 4.11it/s] 63%|βββββββ | 800/1261 [02:52<02:02, 3.77it/s] 64%|βββββββ | 801/1261 [02:52<02:00, 3.83it/s] 64%|βββββββ | 802/1261 [02:52<02:00, 3.80it/s] 64%|βββββββ | 803/1261 [02:53<01:50, 4.15it/s] 64%|βββββββ | 804/1261 [02:53<01:48, 4.22it/s] 64%|βββββββ | 805/1261 [02:53<01:47, 4.24it/s] 64%|βββββββ | 806/1261 [02:53<01:50, 4.12it/s] 64%|βββββββ | 807/1261 [02:54<01:50, 4.12it/s] 64%|βββββββ | 808/1261 [02:54<01:49, 4.13it/s] 64%|βββββββ | 809/1261 [02:54<01:48, 4.16it/s] 64%|βββββββ | 810/1261 [02:54<01:46, 4.23it/s] 64%|βββββββ | 811/1261 [02:54<01:46, 4.23it/s] 64%|βββββββ | 812/1261 [02:55<01:48, 4.15it/s] 64%|βββββββ | 813/1261 [02:55<01:47, 4.18it/s] 65%|βββββββ | 814/1261 [02:55<01:47, 4.16it/s] 65%|βββββββ | 815/1261 [02:55<01:42, 4.36it/s] 65%|βββββββ | 816/1261 [02:56<01:38, 4.52it/s] 65%|βββββββ | 817/1261 [02:56<01:35, 4.64it/s] 65%|βββββββ | 818/1261 [02:56<01:39, 4.47it/s] 65%|βββββββ | 819/1261 [02:56<01:37, 4.53it/s] 65%|βββββββ | 820/1261 [02:56<01:34, 4.65it/s] 65%|βββββββ | 821/1261 [02:57<01:27, 5.02it/s] 65%|βββββββ | 822/1261 [02:57<01:31, 4.81it/s] 65%|βββββββ | 823/1261 [02:57<01:33, 4.68it/s] 65%|βββββββ | 824/1261 [02:57<01:33, 4.67it/s] 65%|βββββββ | 825/1261 [02:58<01:33, 4.67it/s] 66%|βββββββ | 826/1261 [02:58<01:35, 4.53it/s] 66%|βββββββ | 827/1261 [02:58<01:35, 4.56it/s] 66%|βββββββ | 828/1261 [02:58<01:34, 4.57it/s] 66%|βββββββ | 829/1261 [02:58<01:34, 4.59it/s] 66%|βββββββ | 830/1261 [02:59<01:35, 4.52it/s] 66%|βββββββ | 831/1261 [02:59<01:33, 4.58it/s] 66%|βββββββ | 832/1261 [02:59<01:31, 4.69it/s] 66%|βββββββ | 833/1261 [02:59<01:29, 4.78it/s] 66%|βββββββ | 834/1261 [02:59<01:33, 4.57it/s] 66%|βββββββ | 835/1261 [03:00<01:32, 4.59it/s] 66%|βββββββ | 836/1261 [03:00<01:33, 4.54it/s] 66%|βββββββ | 837/1261 [03:00<01:31, 4.62it/s] 66%|βββββββ | 838/1261 [03:00<01:33, 4.50it/s] 67%|βββββββ | 839/1261 [03:01<01:36, 4.37it/s] 67%|βββββββ | 840/1261 [03:01<01:33, 4.52it/s] 67%|βββββββ | 841/1261 [03:01<01:30, 4.62it/s] 67%|βββββββ | 842/1261 [03:01<01:31, 4.57it/s] 67%|βββββββ | 843/1261 [03:01<01:30, 4.61it/s] 67%|βββββββ | 844/1261 [03:02<01:29, 4.66it/s] 67%|βββββββ | 845/1261 [03:02<01:28, 4.72it/s] 67%|βββββββ | 846/1261 [03:02<01:30, 4.58it/s] 67%|βββββββ | 847/1261 [03:02<01:30, 4.58it/s] 67%|βββββββ | 848/1261 [03:03<01:28, 4.65it/s] 67%|βββββββ | 849/1261 [03:03<01:29, 4.58it/s] 67%|βββββββ | 850/1261 [03:03<01:32, 4.43it/s] 67%|βββββββ | 851/1261 [03:03<01:31, 4.49it/s] 68%|βββββββ | 852/1261 [03:03<01:29, 4.58it/s] 68%|βββββββ | 853/1261 [03:04<01:27, 4.68it/s] 68%|βββββββ | 854/1261 [03:04<01:29, 4.53it/s] 68%|βββββββ | 855/1261 [03:04<01:29, 4.51it/s] 68%|βββββββ | 856/1261 [03:04<01:27, 4.60it/s] 68%|βββββββ | 857/1261 [03:05<01:26, 4.65it/s] 68%|βββββββ | 858/1261 [03:05<01:23, 4.80it/s] 68%|βββββββ | 859/1261 [03:05<01:25, 4.71it/s] 68%|βββββββ | 860/1261 [03:05<01:26, 4.63it/s] 68%|βββββββ | 861/1261 [03:05<01:24, 4.72it/s] 68%|βββββββ | 862/1261 [03:06<01:26, 4.61it/s] 68%|βββββββ | 863/1261 [03:06<01:26, 4.62it/s] 69%|βββββββ | 864/1261 [03:06<01:24, 4.71it/s] 69%|βββββββ | 865/1261 [03:06<01:23, 4.76it/s] 69%|βββββββ | 866/1261 [03:06<01:26, 4.59it/s] 69%|βββββββ | 867/1261 [03:07<01:27, 4.50it/s] 69%|βββββββ | 868/1261 [03:07<01:28, 4.43it/s] 69%|βββββββ | 869/1261 [03:07<01:28, 4.44it/s] 69%|βββββββ | 870/1261 [03:07<01:32, 4.22it/s] 69%|βββββββ | 871/1261 [03:08<01:29, 4.34it/s] 69%|βββββββ | 872/1261 [03:08<01:26, 4.49it/s] 69%|βββββββ | 873/1261 [03:08<01:26, 4.50it/s] 69%|βββββββ | 874/1261 [03:08<01:30, 4.30it/s] 69%|βββββββ | 875/1261 [03:08<01:27, 4.39it/s] 69%|βββββββ | 876/1261 [03:09<01:25, 4.51it/s] 70%|βββββββ | 877/1261 [03:09<01:23, 4.59it/s] 70%|βββββββ | 878/1261 [03:09<01:25, 4.49it/s] 70%|βββββββ | 879/1261 [03:09<01:24, 4.51it/s] 70%|βββββββ | 880/1261 [03:10<01:24, 4.53it/s] 70%|βββββββ | 881/1261 [03:10<01:22, 4.62it/s] 70%|βββββββ | 882/1261 [03:10<01:23, 4.53it/s] 70%|βββββββ | 883/1261 [03:10<01:23, 4.54it/s] 70%|βββββββ | 884/1261 [03:10<01:21, 4.62it/s] 70%|βββββββ | 885/1261 [03:11<01:20, 4.69it/s] 70%|βββββββ | 886/1261 [03:11<01:22, 4.53it/s] 70%|βββββββ | 887/1261 [03:11<01:17, 4.85it/s] 70%|βββββββ | 888/1261 [03:11<01:16, 4.86it/s] 70%|βββββββ | 889/1261 [03:11<01:17, 4.83it/s] 71%|βββββββ | 890/1261 [03:12<01:23, 4.46it/s] 71%|βββββββ | 891/1261 [03:12<01:22, 4.50it/s] 71%|βββββββ | 892/1261 [03:12<01:20, 4.60it/s] 71%|βββββββ | 893/1261 [03:12<01:19, 4.64it/s] 71%|βββββββ | 894/1261 [03:13<01:28, 4.15it/s] 71%|βββββββ | 895/1261 [03:13<01:26, 4.22it/s] 71%|βββββββ | 896/1261 [03:13<01:23, 4.39it/s] 71%|βββββββ | 897/1261 [03:13<01:20, 4.50it/s] 71%|βββββββ | 898/1261 [03:14<01:25, 4.23it/s] 71%|ββββββββ | 899/1261 [03:14<01:25, 4.25it/s] 71%|ββββββββ | 900/1261 [03:14<01:23, 4.33it/s] 71%|ββββββββ | 901/1261 [03:14<01:15, 4.76it/s] 72%|ββββββββ | 902/1261 [03:14<01:18, 4.55it/s] 72%|ββββββββ | 903/1261 [03:15<01:22, 4.32it/s] 72%|ββββββββ | 904/1261 [03:15<01:20, 4.42it/s] 72%|ββββββββ | 905/1261 [03:15<01:19, 4.47it/s] 72%|ββββββββ | 906/1261 [03:15<01:20, 4.42it/s] 72%|ββββββββ | 907/1261 [03:16<01:19, 4.48it/s] 72%|ββββββββ | 908/1261 [03:16<01:18, 4.49it/s] 72%|ββββββββ | 909/1261 [03:16<01:21, 4.29it/s] 72%|ββββββββ | 910/1261 [03:16<01:26, 4.06it/s] 72%|ββββββββ | 911/1261 [03:17<01:24, 4.13it/s] 72%|ββββββββ | 912/1261 [03:17<01:21, 4.27it/s] 72%|ββββββββ | 913/1261 [03:17<01:19, 4.40it/s] 72%|ββββββββ | 914/1261 [03:17<01:24, 4.10it/s] 73%|ββββββββ | 915/1261 [03:18<01:23, 4.16it/s] 73%|ββββββββ | 916/1261 [03:18<01:17, 4.45it/s] 73%|ββββββββ | 917/1261 [03:18<01:18, 4.39it/s] 73%|ββββββββ | 918/1261 [03:18<01:19, 4.30it/s] 73%|ββββββββ | 919/1261 [03:18<01:20, 4.27it/s] 73%|ββββββββ | 920/1261 [03:19<01:20, 4.22it/s] 73%|ββββββββ | 921/1261 [03:19<01:21, 4.16it/s] 73%|ββββββββ | 922/1261 [03:19<01:21, 4.14it/s] 73%|ββββββββ | 923/1261 [03:19<01:22, 4.11it/s] 73%|ββββββββ | 924/1261 [03:20<01:20, 4.18it/s] 73%|ββββββββ | 925/1261 [03:20<01:19, 4.23it/s] 73%|ββββββββ | 926/1261 [03:20<01:20, 4.18it/s] 74%|ββββββββ | 927/1261 [03:20<01:17, 4.32it/s] 74%|ββββββββ | 928/1261 [03:21<01:16, 4.36it/s] 74%|ββββββββ | 929/1261 [03:21<01:17, 4.27it/s] 74%|ββββββββ | 930/1261 [03:21<01:20, 4.09it/s] 74%|ββββββββ | 931/1261 [03:21<01:19, 4.14it/s] 74%|ββββββββ | 932/1261 [03:22<01:17, 4.26it/s] 74%|ββββββββ | 933/1261 [03:22<01:15, 4.37it/s] 74%|ββββββββ | 934/1261 [03:22<01:16, 4.29it/s] 74%|ββββββββ | 935/1261 [03:22<01:16, 4.26it/s] 74%|ββββββββ | 936/1261 [03:22<01:17, 4.21it/s] 74%|ββββββββ | 937/1261 [03:23<01:19, 4.09it/s] 74%|ββββββββ | 938/1261 [03:23<01:20, 4.00it/s] 74%|ββββββββ | 939/1261 [03:23<01:17, 4.17it/s] 75%|ββββββββ | 940/1261 [03:23<01:20, 3.99it/s] 75%|ββββββββ | 941/1261 [03:24<01:18, 4.08it/s] 75%|ββββββββ | 942/1261 [03:24<01:26, 3.70it/s] 75%|ββββββββ | 943/1261 [03:24<01:26, 3.69it/s] 75%|ββββββββ | 944/1261 [03:25<01:24, 3.76it/s] 75%|ββββββββ | 945/1261 [03:25<01:23, 3.79it/s] 75%|ββββββββ | 946/1261 [03:25<01:29, 3.53it/s] 75%|ββββββββ | 947/1261 [03:25<01:23, 3.74it/s] 75%|ββββββββ | 948/1261 [03:26<01:23, 3.75it/s] 75%|ββββββββ | 949/1261 [03:26<01:19, 3.91it/s] 75%|ββββββββ | 950/1261 [03:26<01:22, 3.76it/s] 75%|ββββββββ | 951/1261 [03:26<01:20, 3.86it/s] 75%|ββββββββ | 952/1261 [03:27<01:16, 4.04it/s] 76%|ββββββββ | 953/1261 [03:27<01:16, 4.05it/s] 76%|ββββββββ | 954/1261 [03:27<01:17, 3.96it/s] 76%|ββββββββ | 955/1261 [03:27<01:22, 3.70it/s] 76%|ββββββββ | 956/1261 [03:28<01:18, 3.89it/s] 76%|ββββββββ | 957/1261 [03:28<01:18, 3.90it/s] 76%|ββββββββ | 958/1261 [03:28<01:14, 4.06it/s] 76%|ββββββββ | 959/1261 [03:28<01:15, 4.00it/s] 76%|ββββββββ | 960/1261 [03:29<01:14, 4.06it/s] 76%|ββββββββ | 961/1261 [03:29<01:12, 4.13it/s] 76%|ββββββββ | 962/1261 [03:29<01:13, 4.08it/s] 76%|ββββββββ | 963/1261 [03:29<01:13, 4.07it/s] 76%|ββββββββ | 964/1261 [03:30<01:14, 4.01it/s] 77%|ββββββββ | 965/1261 [03:30<01:10, 4.18it/s] 77%|ββββββββ | 966/1261 [03:30<01:11, 4.10it/s] 77%|ββββββββ | 967/1261 [03:30<01:10, 4.15it/s] 77%|ββββββββ | 968/1261 [03:31<01:10, 4.18it/s] 77%|ββββββββ | 969/1261 [03:31<01:07, 4.35it/s] 77%|ββββββββ | 970/1261 [03:31<01:08, 4.22it/s] 77%|ββββββββ | 971/1261 [03:31<01:08, 4.26it/s] 77%|ββββββββ | 972/1261 [03:31<01:07, 4.31it/s] 77%|ββββββββ | 973/1261 [03:32<01:10, 4.09it/s] 77%|ββββββββ | 974/1261 [03:32<01:12, 3.97it/s] 77%|ββββββββ | 975/1261 [03:32<01:10, 4.06it/s] 77%|ββββββββ | 976/1261 [03:33<01:09, 4.09it/s] 77%|ββββββββ | 977/1261 [03:33<01:06, 4.27it/s] 78%|ββββββββ | 978/1261 [03:33<01:08, 4.13it/s] 78%|ββββββββ | 979/1261 [03:33<01:06, 4.25it/s] 78%|ββββββββ | 980/1261 [03:33<01:02, 4.50it/s] 78%|ββββββββ | 981/1261 [03:34<01:01, 4.55it/s] 78%|ββββββββ | 982/1261 [03:34<01:01, 4.52it/s] 78%|ββββββββ | 983/1261 [03:34<01:02, 4.42it/s] 78%|ββββββββ | 984/1261 [03:34<00:59, 4.65it/s] 78%|ββββββββ | 985/1261 [03:34<00:58, 4.69it/s] 78%|ββββββββ | 986/1261 [03:35<00:57, 4.77it/s] 78%|ββββββββ | 987/1261 [03:35<00:57, 4.76it/s] 78%|ββββββββ | 988/1261 [03:35<00:55, 4.90it/s] 78%|ββββββββ | 989/1261 [03:35<00:54, 4.95it/s] 79%|ββββββββ | 990/1261 [03:35<00:55, 4.86it/s] 79%|ββββββββ | 991/1261 [03:36<00:56, 4.82it/s] 79%|ββββββββ | 992/1261 [03:36<00:54, 4.92it/s] 79%|ββββββββ | 993/1261 [03:36<00:54, 4.92it/s] 79%|ββββββββ | 994/1261 [03:36<00:55, 4.80it/s] 79%|ββββββββ | 995/1261 [03:37<00:54, 4.86it/s] 79%|ββββββββ | 996/1261 [03:37<00:53, 4.91it/s] 79%|ββββββββ | 997/1261 [03:37<00:53, 4.97it/s] 79%|ββββββββ | 998/1261 [03:37<00:53, 4.91it/s] 79%|ββββββββ | 999/1261 [03:37<00:52, 4.96it/s] 79%|ββββββββ | 1000/1261 [03:38<00:52, 4.97it/s] 79%|ββββββββ | 1001/1261 [03:38<00:53, 4.90it/s] 79%|ββββββββ | 1002/1261 [03:38<00:53, 4.80it/s] 80%|ββββββββ | 1003/1261 [03:38<00:53, 4.86it/s] 80%|ββββββββ | 1004/1261 [03:38<00:52, 4.88it/s] 80%|ββββββββ | 1005/1261 [03:39<00:52, 4.86it/s] 80%|ββββββββ | 1006/1261 [03:39<00:56, 4.52it/s] 80%|ββββββββ | 1007/1261 [03:39<00:55, 4.54it/s] 80%|ββββββββ | 1008/1261 [03:39<00:55, 4.56it/s] 80%|ββββββββ | 1009/1261 [03:39<00:53, 4.67it/s] 80%|ββββββββ | 1010/1261 [03:40<00:55, 4.53it/s] 80%|ββββββββ | 1011/1261 [03:40<00:55, 4.54it/s] 80%|ββββββββ | 1012/1261 [03:40<00:51, 4.79it/s] 80%|ββββββββ | 1013/1261 [03:40<00:51, 4.78it/s] 80%|ββββββββ | 1014/1261 [03:40<00:50, 4.86it/s] 80%|ββββββββ | 1015/1261 [03:41<00:51, 4.74it/s] 81%|ββββββββ | 1016/1261 [03:41<00:50, 4.88it/s] 81%|ββββββββ | 1017/1261 [03:41<00:50, 4.81it/s] 81%|ββββββββ | 1018/1261 [03:41<00:50, 4.81it/s] 81%|ββββββββ | 1019/1261 [03:42<00:50, 4.81it/s] 81%|ββββββββ | 1020/1261 [03:42<00:48, 5.00it/s] 81%|ββββββββ | 1021/1261 [03:42<00:46, 5.21it/s] 81%|ββββββββ | 1022/1261 [03:42<00:46, 5.16it/s] 81%|ββββββββ | 1023/1261 [03:42<00:44, 5.31it/s] 81%|ββββββββ | 1024/1261 [03:42<00:45, 5.17it/s] 81%|βββββββββ | 1025/1261 [03:43<00:47, 4.99it/s] 81%|βββββββββ | 1026/1261 [03:43<00:52, 4.48it/s] 81%|βββββββββ | 1027/1261 [03:43<00:49, 4.76it/s] 82%|βββββββββ | 1028/1261 [03:43<00:47, 4.96it/s] 82%|βββββββββ | 1029/1261 [03:44<00:47, 4.87it/s] 82%|βββββββββ | 1030/1261 [03:44<00:49, 4.65it/s] 82%|βββββββββ | 1031/1261 [03:44<00:50, 4.59it/s] 82%|βββββββββ | 1032/1261 [03:44<00:50, 4.56it/s] 82%|βββββββββ | 1033/1261 [03:44<00:49, 4.59it/s] 82%|βββββββββ | 1034/1261 [03:45<00:51, 4.37it/s] 82%|βββββββββ | 1035/1261 [03:45<00:52, 4.33it/s] 82%|βββββββββ | 1036/1261 [03:45<00:49, 4.53it/s] 82%|βββββββββ | 1037/1261 [03:45<00:46, 4.85it/s] 82%|βββββββββ | 1038/1261 [03:46<00:48, 4.60it/s] 82%|βββββββββ | 1039/1261 [03:46<00:48, 4.54it/s] 82%|βββββββββ | 1040/1261 [03:46<00:49, 4.47it/s] 83%|βββββββββ | 1041/1261 [03:46<00:49, 4.44it/s] 83%|βββββββββ | 1042/1261 [03:46<00:49, 4.41it/s] 83%|βββββββββ | 1043/1261 [03:47<00:50, 4.32it/s] 83%|βββββββββ | 1044/1261 [03:47<00:50, 4.32it/s] 83%|βββββββββ | 1045/1261 [03:47<00:50, 4.24it/s] 83%|βββββββββ | 1046/1261 [03:47<00:51, 4.21it/s] 83%|βββββββββ | 1047/1261 [03:48<00:50, 4.23it/s] 83%|βββββββββ | 1048/1261 [03:48<00:50, 4.20it/s] 83%|βββββββββ | 1049/1261 [03:48<00:49, 4.26it/s] 83%|βββββββββ | 1050/1261 [03:48<00:48, 4.35it/s] 83%|βββββββββ | 1051/1261 [03:49<00:47, 4.38it/s] 83%|βββββββββ | 1052/1261 [03:49<00:49, 4.23it/s] 84%|βββββββββ | 1053/1261 [03:49<00:49, 4.23it/s] 84%|βββββββββ | 1054/1261 [03:49<00:47, 4.31it/s] 84%|βββββββββ | 1055/1261 [03:50<00:48, 4.28it/s] 84%|βββββββββ | 1056/1261 [03:50<00:51, 3.98it/s] 84%|βββββββββ | 1057/1261 [03:50<00:50, 4.05it/s] 84%|βββββββββ | 1058/1261 [03:50<00:49, 4.14it/s] 84%|βββββββββ | 1059/1261 [03:50<00:47, 4.24it/s] 84%|βββββββββ | 1060/1261 [03:51<00:47, 4.19it/s] 84%|βββββββββ | 1061/1261 [03:51<00:46, 4.33it/s] 84%|βββββββββ | 1062/1261 [03:51<00:45, 4.34it/s] 84%|βββββββββ | 1063/1261 [03:51<00:45, 4.35it/s] 84%|βββββββββ | 1064/1261 [03:52<00:46, 4.24it/s] 84%|βββββββββ | 1065/1261 [03:52<00:47, 4.09it/s] 85%|βββββββββ | 1066/1261 [03:52<00:43, 4.45it/s] 85%|βββββββββ | 1067/1261 [03:52<00:43, 4.46it/s] 85%|βββββββββ | 1068/1261 [03:53<00:44, 4.30it/s] 85%|βββββββββ | 1069/1261 [03:53<00:44, 4.30it/s] 85%|βββββββββ | 1070/1261 [03:53<00:44, 4.32it/s] 85%|βββββββββ | 1071/1261 [03:53<00:43, 4.32it/s] 85%|βββββββββ | 1072/1261 [03:54<00:45, 4.16it/s] 85%|βββββββββ | 1073/1261 [03:54<00:45, 4.14it/s] 85%|βββββββββ | 1074/1261 [03:54<00:45, 4.12it/s] 85%|βββββββββ | 1075/1261 [03:54<00:44, 4.19it/s] 85%|βββββββββ | 1076/1261 [03:55<00:45, 4.08it/s] 85%|βββββββββ | 1077/1261 [03:55<00:44, 4.12it/s] 85%|βββββββββ | 1078/1261 [03:55<00:43, 4.22it/s] 86%|βββββββββ | 1079/1261 [03:55<00:42, 4.25it/s] 86%|βββββββββ | 1080/1261 [03:55<00:43, 4.17it/s] 86%|βββββββββ | 1081/1261 [03:56<00:43, 4.17it/s] 86%|βββββββββ | 1082/1261 [03:56<00:42, 4.24it/s] 86%|βββββββββ | 1083/1261 [03:56<00:42, 4.20it/s] 86%|βββββββββ | 1084/1261 [03:56<00:42, 4.14it/s] 86%|βββββββββ | 1085/1261 [03:57<00:42, 4.19it/s] 86%|βββββββββ | 1086/1261 [03:57<00:41, 4.20it/s] 86%|βββββββββ | 1087/1261 [03:57<00:42, 4.13it/s] 86%|βββββββββ | 1088/1261 [03:57<00:41, 4.13it/s] 86%|βββββββββ | 1089/1261 [03:58<00:41, 4.19it/s] 86%|βββββββββ | 1090/1261 [03:58<00:40, 4.23it/s] 87%|βββββββββ | 1091/1261 [03:58<00:41, 4.11it/s] 87%|βββββββββ | 1092/1261 [03:58<00:41, 4.12it/s] 87%|βββββββββ | 1093/1261 [03:59<00:40, 4.13it/s] 87%|βββββββββ | 1094/1261 [03:59<00:39, 4.25it/s] 87%|βββββββββ | 1095/1261 [03:59<00:39, 4.22it/s] 87%|βββββββββ | 1096/1261 [03:59<00:38, 4.26it/s] 87%|βββββββββ | 1097/1261 [03:59<00:37, 4.34it/s] 87%|βββββββββ | 1098/1261 [04:00<00:37, 4.40it/s] 87%|βββββββββ | 1099/1261 [04:00<00:36, 4.41it/s] 87%|βββββββββ | 1100/1261 [04:00<00:37, 4.29it/s] 87%|βββββββββ | 1101/1261 [04:00<00:36, 4.33it/s] 87%|βββββββββ | 1102/1261 [04:01<00:36, 4.36it/s] 87%|βββββββββ | 1103/1261 [04:01<00:36, 4.39it/s] 88%|βββββββββ | 1104/1261 [04:01<00:37, 4.23it/s] 88%|βββββββββ | 1105/1261 [04:01<00:36, 4.31it/s] 88%|βββββββββ | 1106/1261 [04:02<00:35, 4.39it/s] 88%|βββββββββ | 1107/1261 [04:02<00:34, 4.47it/s] 88%|βββββββββ | 1108/1261 [04:02<00:35, 4.34it/s] 88%|βββββββββ | 1109/1261 [04:02<00:34, 4.37it/s] 88%|βββββββββ | 1110/1261 [04:02<00:34, 4.39it/s] 88%|βββββββββ | 1111/1261 [04:03<00:33, 4.43it/s] 88%|βββββββββ | 1112/1261 [04:03<00:35, 4.22it/s] 88%|βββββββββ | 1113/1261 [04:03<00:34, 4.25it/s] 88%|βββββββββ | 1114/1261 [04:03<00:34, 4.31it/s] 88%|βββββββββ | 1115/1261 [04:04<00:33, 4.37it/s] 89%|βββββββββ | 1116/1261 [04:04<00:33, 4.33it/s] 89%|βββββββββ | 1117/1261 [04:04<00:33, 4.35it/s] 89%|βββββββββ | 1118/1261 [04:04<00:32, 4.45it/s] 89%|βββββββββ | 1119/1261 [04:05<00:31, 4.52it/s] 89%|βββββββββ | 1120/1261 [04:05<00:32, 4.38it/s] 89%|βββββββββ | 1121/1261 [04:05<00:32, 4.36it/s] 89%|βββββββββ | 1122/1261 [04:05<00:32, 4.32it/s] 89%|βββββββββ | 1123/1261 [04:05<00:31, 4.40it/s] 89%|βββββββββ | 1124/1261 [04:06<00:31, 4.34it/s] 89%|βββββββββ | 1125/1261 [04:06<00:31, 4.36it/s] 89%|βββββββββ | 1126/1261 [04:06<00:30, 4.44it/s] 89%|βββββββββ | 1127/1261 [04:06<00:29, 4.49it/s] 89%|βββββββββ | 1128/1261 [04:07<00:30, 4.40it/s] 90%|βββββββββ | 1129/1261 [04:07<00:29, 4.45it/s] 90%|βββββββββ | 1130/1261 [04:07<00:29, 4.47it/s] 90%|βββββββββ | 1131/1261 [04:07<00:28, 4.49it/s] 90%|βββββββββ | 1132/1261 [04:08<00:30, 4.28it/s] 90%|βββββββββ | 1133/1261 [04:08<00:29, 4.30it/s] 90%|βββββββββ | 1134/1261 [04:08<00:29, 4.35it/s] 90%|βββββββββ | 1135/1261 [04:08<00:28, 4.39it/s] 90%|βββββββββ | 1136/1261 [04:08<00:28, 4.31it/s] 90%|βββββββββ | 1137/1261 [04:09<00:28, 4.35it/s] 90%|βββββββββ | 1138/1261 [04:09<00:27, 4.45it/s] 90%|βββββββββ | 1139/1261 [04:09<00:27, 4.51it/s] 90%|βββββββββ | 1140/1261 [04:09<00:27, 4.41it/s] 90%|βββββββββ | 1141/1261 [04:10<00:27, 4.39it/s] 91%|βββββββββ | 1142/1261 [04:10<00:26, 4.41it/s] 91%|βββββββββ | 1143/1261 [04:10<00:26, 4.48it/s] 91%|βββββββββ | 1144/1261 [04:10<00:26, 4.39it/s] 91%|βββββββββ | 1145/1261 [04:10<00:26, 4.40it/s] 91%|βββββββββ | 1146/1261 [04:11<00:25, 4.50it/s] 91%|βββββββββ | 1147/1261 [04:11<00:25, 4.51it/s] 91%|βββββββββ | 1148/1261 [04:11<00:26, 4.33it/s] 91%|βββββββββ | 1149/1261 [04:11<00:25, 4.34it/s] 91%|βββββββββ | 1150/1261 [04:12<00:25, 4.35it/s] 91%|ββββββββββ| 1151/1261 [04:12<00:25, 4.34it/s] 91%|ββββββββββ| 1152/1261 [04:12<00:26, 4.12it/s] 91%|ββββββββββ| 1153/1261 [04:12<00:25, 4.19it/s] 92%|ββββββββββ| 1154/1261 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[MoviePy] Done. [MoviePy] >>>> Video ready: project_video_out.mp4 CPU times: user 7min 13s, sys: 1min 2s, total: 8min 16s Wall time: 4min 40s
In [643]:
clip2= VideoFileClip("../CarND-Advanced-Lane-Lines/harder_challenge_video.mp4")
white_output = 'harder_challenge_out.mp4'
white_clip = clip2.fl_image(pipeline)
%time white_clip.write_videofile(white_output, audio=False)
[MoviePy] >>>> Building video harder_challenge_out.mp4 [MoviePy] Writing video harder_challenge_out.mp4
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3.94it/s] 9%|β | 103/1200 [00:22<04:44, 3.86it/s] 9%|β | 104/1200 [00:22<04:44, 3.85it/s] 9%|β | 105/1200 [00:22<04:35, 3.98it/s] 9%|β | 106/1200 [00:22<04:22, 4.17it/s] 9%|β | 107/1200 [00:23<04:16, 4.26it/s] 9%|β | 108/1200 [00:23<04:14, 4.28it/s] 9%|β | 109/1200 [00:23<04:09, 4.38it/s] 9%|β | 110/1200 [00:23<04:03, 4.48it/s] 9%|β | 111/1200 [00:23<04:02, 4.49it/s] 9%|β | 112/1200 [00:24<03:45, 4.82it/s] 9%|β | 113/1200 [00:24<03:36, 5.03it/s] 10%|β | 114/1200 [00:24<03:20, 5.41it/s] 10%|β | 115/1200 [00:24<03:26, 5.24it/s] 10%|β | 116/1200 [00:24<03:30, 5.15it/s] 10%|β | 117/1200 [00:25<03:32, 5.10it/s] 10%|β | 118/1200 [00:25<03:33, 5.08it/s] 10%|β | 119/1200 [00:25<03:43, 4.85it/s] 10%|β | 120/1200 [00:25<03:54, 4.62it/s] 10%|β | 121/1200 [00:25<03:56, 4.57it/s] 10%|β | 122/1200 [00:26<04:16, 4.20it/s] 10%|β | 123/1200 [00:26<04:44, 3.78it/s] 10%|β | 124/1200 [00:26<04:43, 3.80it/s] 10%|β | 125/1200 [00:27<04:40, 3.84it/s] 10%|β | 126/1200 [00:27<05:16, 3.40it/s] 11%|β | 127/1200 [00:27<05:24, 3.31it/s] 11%|β | 128/1200 [00:28<05:25, 3.29it/s] 11%|β | 129/1200 [00:28<05:22, 3.32it/s] 11%|β | 130/1200 [00:28<05:36, 3.18it/s] 11%|β | 131/1200 [00:29<05:43, 3.11it/s] 11%|β | 132/1200 [00:29<06:02, 2.94it/s] 11%|β | 133/1200 [00:29<06:02, 2.95it/s] 11%|β | 134/1200 [00:30<06:41, 2.65it/s] 11%|ββ | 135/1200 [00:30<07:16, 2.44it/s] 11%|ββ | 136/1200 [00:31<07:39, 2.31it/s] 11%|ββ | 137/1200 [00:31<07:19, 2.42it/s] 12%|ββ | 138/1200 [00:32<07:36, 2.33it/s] 12%|ββ | 139/1200 [00:32<07:14, 2.44it/s] 12%|ββ | 140/1200 [00:32<06:58, 2.53it/s] 12%|ββ | 141/1200 [00:33<06:31, 2.71it/s] 12%|ββ | 142/1200 [00:33<06:58, 2.53it/s] 12%|ββ | 143/1200 [00:33<06:10, 2.85it/s] 12%|ββ | 144/1200 [00:34<05:37, 3.13it/s] 12%|ββ | 145/1200 [00:34<05:23, 3.26it/s] 12%|ββ | 146/1200 [00:34<05:26, 3.23it/s] 12%|ββ | 147/1200 [00:34<05:01, 3.50it/s] 12%|ββ | 148/1200 [00:35<04:36, 3.81it/s] 12%|ββ | 149/1200 [00:35<04:15, 4.11it/s] 12%|ββ | 150/1200 [00:35<04:05, 4.27it/s] 13%|ββ | 151/1200 [00:35<03:54, 4.46it/s] 13%|ββ | 152/1200 [00:35<03:47, 4.60it/s] 13%|ββ | 153/1200 [00:36<03:39, 4.76it/s] 13%|ββ | 154/1200 [00:36<03:35, 4.85it/s] 13%|ββ | 155/1200 [00:36<03:38, 4.78it/s] 13%|ββ | 156/1200 [00:36<03:37, 4.80it/s] 13%|ββ | 157/1200 [00:36<03:32, 4.92it/s] 13%|ββ | 158/1200 [00:37<03:27, 5.03it/s] 13%|ββ | 159/1200 [00:37<03:32, 4.91it/s] 13%|ββ | 160/1200 [00:37<04:04, 4.25it/s] 13%|ββ | 161/1200 [00:37<04:04, 4.25it/s] 14%|ββ | 162/1200 [00:38<04:00, 4.31it/s] 14%|ββ | 163/1200 [00:38<03:59, 4.32it/s] 14%|ββ | 164/1200 [00:38<03:55, 4.40it/s] 14%|ββ | 165/1200 [00:38<03:45, 4.59it/s] 14%|ββ | 166/1200 [00:38<03:39, 4.71it/s] 14%|ββ | 167/1200 [00:39<03:42, 4.65it/s] 14%|ββ | 168/1200 [00:39<03:47, 4.53it/s] 14%|ββ | 169/1200 [00:39<03:40, 4.67it/s] 14%|ββ | 170/1200 [00:39<03:44, 4.58it/s] 14%|ββ | 171/1200 [00:40<03:49, 4.47it/s] 14%|ββ | 172/1200 [00:40<03:41, 4.64it/s] 14%|ββ | 173/1200 [00:40<03:38, 4.71it/s] 14%|ββ | 174/1200 [00:40<03:37, 4.71it/s] 15%|ββ | 175/1200 [00:40<03:42, 4.60it/s] 15%|ββ | 176/1200 [00:41<03:41, 4.63it/s] 15%|ββ | 177/1200 [00:41<03:31, 4.83it/s] 15%|ββ | 178/1200 [00:41<03:25, 4.96it/s] 15%|ββ | 179/1200 [00:41<03:27, 4.92it/s] 15%|ββ | 180/1200 [00:41<03:24, 4.99it/s] 15%|ββ | 181/1200 [00:42<03:19, 5.11it/s] 15%|ββ | 182/1200 [00:42<03:21, 5.05it/s] 15%|ββ | 183/1200 [00:42<03:31, 4.80it/s] 15%|ββ | 184/1200 [00:42<03:37, 4.67it/s] 15%|ββ | 185/1200 [00:42<03:37, 4.67it/s] 16%|ββ | 186/1200 [00:43<03:40, 4.59it/s] 16%|ββ | 187/1200 [00:43<03:47, 4.46it/s] 16%|ββ | 188/1200 [00:43<03:40, 4.59it/s] 16%|ββ | 189/1200 [00:43<03:36, 4.66it/s] 16%|ββ | 190/1200 [00:44<03:35, 4.68it/s] 16%|ββ | 191/1200 [00:44<03:43, 4.51it/s] 16%|ββ | 192/1200 [00:44<03:48, 4.41it/s] 16%|ββ | 193/1200 [00:44<03:40, 4.57it/s] 16%|ββ | 194/1200 [00:44<03:34, 4.68it/s] 16%|ββ | 195/1200 [00:45<03:40, 4.56it/s] 16%|ββ | 196/1200 [00:45<03:39, 4.57it/s] 16%|ββ | 197/1200 [00:45<03:42, 4.52it/s] 16%|ββ | 198/1200 [00:45<03:40, 4.55it/s] 17%|ββ | 199/1200 [00:46<03:49, 4.36it/s] 17%|ββ | 200/1200 [00:46<03:52, 4.31it/s] 17%|ββ | 201/1200 [00:46<03:48, 4.37it/s] 17%|ββ | 202/1200 [00:46<03:52, 4.29it/s] 17%|ββ | 203/1200 [00:47<04:04, 4.08it/s] 17%|ββ | 204/1200 [00:47<04:06, 4.04it/s] 17%|ββ | 205/1200 [00:47<04:05, 4.04it/s] 17%|ββ | 206/1200 [00:47<04:07, 4.02it/s] 17%|ββ | 207/1200 [00:48<04:12, 3.93it/s] 17%|ββ | 208/1200 [00:48<04:13, 3.91it/s] 17%|ββ | 209/1200 [00:48<04:10, 3.95it/s] 18%|ββ | 210/1200 [00:48<04:08, 3.98it/s] 18%|ββ | 211/1200 [00:49<04:15, 3.88it/s] 18%|ββ | 212/1200 [00:49<04:10, 3.94it/s] 18%|ββ | 213/1200 [00:49<04:05, 4.03it/s] 18%|ββ | 214/1200 [00:49<04:01, 4.09it/s] 18%|ββ | 215/1200 [00:50<04:07, 3.99it/s] 18%|ββ | 216/1200 [00:50<04:10, 3.93it/s] 18%|ββ | 217/1200 [00:50<04:11, 3.91it/s] 18%|ββ | 218/1200 [00:50<04:10, 3.92it/s] 18%|ββ | 219/1200 [00:51<04:15, 3.83it/s] 18%|ββ | 220/1200 [00:51<04:16, 3.82it/s] 18%|ββ | 221/1200 [00:51<04:15, 3.84it/s] 18%|ββ | 222/1200 [00:51<04:20, 3.76it/s] 19%|ββ | 223/1200 [00:52<04:21, 3.74it/s] 19%|ββ | 224/1200 [00:52<04:17, 3.80it/s] 19%|ββ | 225/1200 [00:52<04:12, 3.86it/s] 19%|ββ | 226/1200 [00:52<04:05, 3.97it/s] 19%|ββ | 227/1200 [00:53<04:10, 3.88it/s] 19%|ββ | 228/1200 [00:53<04:16, 3.79it/s] 19%|ββ | 229/1200 [00:53<04:17, 3.77it/s] 19%|ββ | 230/1200 [00:53<04:14, 3.81it/s] 19%|ββ | 231/1200 [00:54<04:25, 3.64it/s] 19%|ββ | 232/1200 [00:54<04:18, 3.74it/s] 19%|ββ | 233/1200 [00:54<04:15, 3.78it/s] 20%|ββ | 234/1200 [00:55<04:11, 3.84it/s] 20%|ββ | 235/1200 [00:55<04:16, 3.76it/s] 20%|ββ | 236/1200 [00:55<04:21, 3.68it/s] 20%|ββ | 237/1200 [00:55<04:14, 3.78it/s] 20%|ββ | 238/1200 [00:56<04:01, 3.98it/s] 20%|ββ | 239/1200 [00:56<04:00, 3.99it/s] 20%|ββ | 240/1200 [00:56<03:53, 4.11it/s] 20%|ββ | 241/1200 [00:56<03:50, 4.16it/s] 20%|ββ | 242/1200 [00:57<03:50, 4.15it/s] 20%|ββ | 243/1200 [00:57<03:48, 4.18it/s] 20%|ββ | 244/1200 [00:57<03:45, 4.24it/s] 20%|ββ | 245/1200 [00:57<03:52, 4.11it/s] 20%|ββ | 246/1200 [00:58<03:54, 4.07it/s] 21%|ββ | 247/1200 [00:58<03:51, 4.12it/s] 21%|ββ | 248/1200 [00:58<03:47, 4.18it/s] 21%|ββ | 249/1200 [00:58<03:47, 4.17it/s] 21%|ββ | 250/1200 [00:58<03:52, 4.09it/s] 21%|ββ | 251/1200 [00:59<04:01, 3.92it/s] 21%|ββ | 252/1200 [00:59<04:29, 3.51it/s] 21%|ββ | 253/1200 [00:59<04:24, 3.58it/s] 21%|ββ | 254/1200 [01:00<04:31, 3.49it/s] 21%|βββ | 255/1200 [01:00<04:32, 3.46it/s] 21%|βββ | 256/1200 [01:00<04:37, 3.41it/s] 21%|βββ | 257/1200 [01:01<04:33, 3.44it/s] 22%|βββ | 258/1200 [01:01<04:32, 3.45it/s] 22%|βββ | 259/1200 [01:01<05:07, 3.06it/s] 22%|βββ | 260/1200 [01:02<05:57, 2.63it/s] 22%|βββ | 261/1200 [01:02<05:36, 2.79it/s] 22%|βββ | 262/1200 [01:02<05:06, 3.06it/s] 22%|βββ | 263/1200 [01:03<04:58, 3.14it/s] 22%|βββ | 264/1200 [01:03<04:47, 3.26it/s] 22%|βββ | 265/1200 [01:03<04:41, 3.32it/s] 22%|βββ | 266/1200 [01:03<04:31, 3.44it/s] 22%|βββ | 267/1200 [01:04<04:18, 3.61it/s] 22%|βββ | 268/1200 [01:04<04:15, 3.65it/s] 22%|βββ | 269/1200 [01:04<04:19, 3.59it/s] 22%|βββ | 270/1200 [01:05<04:11, 3.70it/s] 23%|βββ | 271/1200 [01:05<04:08, 3.74it/s] 23%|βββ | 272/1200 [01:05<04:08, 3.73it/s] 23%|βββ | 273/1200 [01:05<04:08, 3.73it/s] 23%|βββ | 274/1200 [01:06<03:58, 3.88it/s] 23%|βββ | 275/1200 [01:06<03:54, 3.95it/s] 23%|βββ | 276/1200 [01:06<03:55, 3.92it/s] 23%|βββ | 277/1200 [01:06<03:58, 3.87it/s] 23%|βββ | 278/1200 [01:07<04:00, 3.83it/s] 23%|βββ | 279/1200 [01:07<04:05, 3.76it/s] 23%|βββ | 280/1200 [01:07<04:17, 3.57it/s] 23%|βββ | 281/1200 [01:08<04:34, 3.35it/s] 24%|βββ | 282/1200 [01:08<04:21, 3.51it/s] 24%|βββ | 283/1200 [01:08<04:04, 3.75it/s] 24%|βββ | 284/1200 [01:08<03:52, 3.95it/s] 24%|βββ | 285/1200 [01:08<03:48, 4.01it/s] 24%|βββ | 286/1200 [01:09<03:47, 4.02it/s] 24%|βββ | 287/1200 [01:09<03:37, 4.20it/s] 24%|βββ | 288/1200 [01:09<03:33, 4.27it/s] 24%|βββ | 289/1200 [01:09<03:33, 4.27it/s] 24%|βββ | 290/1200 [01:10<03:49, 3.97it/s] 24%|βββ | 291/1200 [01:10<03:42, 4.09it/s] 24%|βββ | 292/1200 [01:10<03:45, 4.02it/s] 24%|βββ | 293/1200 [01:10<03:35, 4.20it/s] 24%|βββ | 294/1200 [01:11<03:33, 4.25it/s] 25%|βββ | 295/1200 [01:11<03:34, 4.22it/s] 25%|βββ | 296/1200 [01:11<03:29, 4.32it/s] 25%|βββ | 297/1200 [01:11<03:30, 4.28it/s] 25%|βββ | 298/1200 [01:12<03:28, 4.32it/s] 25%|βββ | 299/1200 [01:12<03:27, 4.35it/s] 25%|βββ | 300/1200 [01:12<03:23, 4.43it/s] 25%|βββ | 301/1200 [01:12<03:20, 4.49it/s] 25%|βββ | 302/1200 [01:12<03:28, 4.31it/s] 25%|βββ | 303/1200 [01:13<03:29, 4.29it/s] 25%|βββ | 304/1200 [01:13<03:24, 4.38it/s] 25%|βββ | 305/1200 [01:13<03:20, 4.46it/s] 26%|βββ | 306/1200 [01:13<03:23, 4.38it/s] 26%|βββ | 307/1200 [01:14<03:17, 4.52it/s] 26%|βββ | 308/1200 [01:14<03:14, 4.58it/s] 26%|βββ | 309/1200 [01:14<03:06, 4.79it/s] 26%|βββ | 310/1200 [01:14<03:12, 4.62it/s] 26%|βββ | 311/1200 [01:14<03:22, 4.40it/s] 26%|βββ | 312/1200 [01:15<03:32, 4.18it/s] 26%|βββ | 313/1200 [01:15<03:31, 4.20it/s] 26%|βββ | 314/1200 [01:15<03:36, 4.09it/s] 26%|βββ | 315/1200 [01:15<03:40, 4.02it/s] 26%|βββ | 316/1200 [01:16<03:50, 3.83it/s] 26%|βββ | 317/1200 [01:16<03:48, 3.87it/s] 26%|βββ | 318/1200 [01:16<03:43, 3.94it/s] 27%|βββ | 319/1200 [01:17<03:49, 3.83it/s] 27%|βββ | 320/1200 [01:17<03:43, 3.93it/s] 27%|βββ | 321/1200 [01:17<03:49, 3.82it/s] 27%|βββ | 322/1200 [01:17<03:41, 3.96it/s] 27%|βββ | 323/1200 [01:17<03:39, 4.00it/s] 27%|βββ | 324/1200 [01:18<03:24, 4.28it/s] 27%|βββ | 325/1200 [01:18<03:25, 4.26it/s] 27%|βββ | 326/1200 [01:18<03:29, 4.17it/s] 27%|βββ | 327/1200 [01:18<03:40, 3.96it/s] 27%|βββ | 328/1200 [01:19<03:33, 4.09it/s] 27%|βββ | 329/1200 [01:19<03:22, 4.30it/s] 28%|βββ | 330/1200 [01:19<03:14, 4.46it/s] 28%|βββ | 331/1200 [01:19<03:15, 4.45it/s] 28%|βββ | 332/1200 [01:20<03:16, 4.42it/s] 28%|βββ | 333/1200 [01:20<03:15, 4.44it/s] 28%|βββ | 334/1200 [01:20<03:09, 4.56it/s] 28%|βββ | 335/1200 [01:20<03:08, 4.58it/s] 28%|βββ | 336/1200 [01:20<03:13, 4.46it/s] 28%|βββ | 337/1200 [01:21<03:17, 4.36it/s] 28%|βββ | 338/1200 [01:21<03:12, 4.47it/s] 28%|βββ | 339/1200 [01:21<03:20, 4.30it/s] 28%|βββ | 340/1200 [01:21<03:18, 4.33it/s] 28%|βββ | 341/1200 [01:22<03:13, 4.43it/s] 28%|βββ | 342/1200 [01:22<03:13, 4.42it/s] 29%|βββ | 343/1200 [01:22<03:10, 4.50it/s] 29%|βββ | 344/1200 [01:22<03:02, 4.69it/s] 29%|βββ | 345/1200 [01:22<02:58, 4.78it/s] 29%|βββ | 346/1200 [01:23<02:57, 4.82it/s] 29%|βββ | 347/1200 [01:23<03:04, 4.61it/s] 29%|βββ | 348/1200 [01:23<03:04, 4.61it/s] 29%|βββ | 349/1200 [01:23<03:03, 4.63it/s] 29%|βββ | 350/1200 [01:24<03:06, 4.57it/s] 29%|βββ | 351/1200 [01:24<03:33, 3.98it/s] 29%|βββ | 352/1200 [01:24<03:38, 3.88it/s] 29%|βββ | 353/1200 [01:24<03:34, 3.96it/s] 30%|βββ | 354/1200 [01:25<03:26, 4.09it/s] 30%|βββ | 355/1200 [01:25<03:17, 4.29it/s] 30%|βββ | 356/1200 [01:25<03:11, 4.40it/s] 30%|βββ | 357/1200 [01:25<03:01, 4.66it/s] 30%|βββ | 358/1200 [01:25<03:01, 4.64it/s] 30%|βββ | 359/1200 [01:26<02:55, 4.80it/s] 30%|βββ | 360/1200 [01:26<03:05, 4.53it/s] 30%|βββ | 361/1200 [01:26<03:17, 4.25it/s] 30%|βββ | 362/1200 [01:26<03:25, 4.07it/s] 30%|βββ | 363/1200 [01:27<03:19, 4.19it/s] 30%|βββ | 364/1200 [01:27<03:30, 3.98it/s] 30%|βββ | 365/1200 [01:27<03:39, 3.81it/s] 30%|βββ | 366/1200 [01:28<04:00, 3.47it/s] 31%|βββ | 367/1200 [01:28<03:49, 3.64it/s] 31%|βββ | 368/1200 [01:28<03:48, 3.64it/s] 31%|βββ | 369/1200 [01:28<03:47, 3.66it/s] 31%|βββ | 370/1200 [01:29<03:44, 3.70it/s] 31%|βββ | 371/1200 [01:29<03:43, 3.71it/s] 31%|βββ | 372/1200 [01:29<03:38, 3.79it/s] 31%|βββ | 373/1200 [01:29<03:33, 3.87it/s] 31%|βββ | 374/1200 [01:30<03:29, 3.95it/s] 31%|ββββ | 375/1200 [01:30<03:40, 3.75it/s] 31%|ββββ | 376/1200 [01:30<03:47, 3.62it/s] 31%|ββββ | 377/1200 [01:30<03:41, 3.72it/s] 32%|ββββ | 378/1200 [01:31<03:42, 3.70it/s] 32%|ββββ | 379/1200 [01:31<03:47, 3.61it/s] 32%|ββββ | 380/1200 [01:31<03:46, 3.62it/s] 32%|ββββ | 381/1200 [01:32<03:43, 3.67it/s] 32%|ββββ | 382/1200 [01:32<03:47, 3.60it/s] 32%|ββββ | 383/1200 [01:32<03:44, 3.64it/s] 32%|ββββ | 384/1200 [01:32<03:56, 3.45it/s] 32%|ββββ | 385/1200 [01:33<03:51, 3.52it/s] 32%|ββββ | 386/1200 [01:33<03:48, 3.56it/s] 32%|ββββ | 387/1200 [01:33<03:57, 3.42it/s] 32%|ββββ | 388/1200 [01:34<04:02, 3.35it/s] 32%|ββββ | 389/1200 [01:34<04:00, 3.37it/s] 32%|ββββ | 390/1200 [01:34<03:59, 3.38it/s] 33%|ββββ | 391/1200 [01:35<04:21, 3.10it/s] 33%|ββββ | 392/1200 [01:35<04:45, 2.83it/s] 33%|ββββ | 393/1200 [01:35<04:51, 2.77it/s] 33%|ββββ | 394/1200 [01:36<04:37, 2.90it/s] 33%|ββββ | 395/1200 [01:36<04:25, 3.03it/s] 33%|ββββ | 396/1200 [01:36<04:15, 3.14it/s] 33%|ββββ | 397/1200 [01:37<04:11, 3.19it/s] 33%|ββββ | 398/1200 [01:37<04:06, 3.25it/s] 33%|ββββ | 399/1200 [01:37<04:01, 3.32it/s] 33%|ββββ | 400/1200 [01:37<03:56, 3.38it/s] 33%|ββββ | 401/1200 [01:38<04:05, 3.26it/s] 34%|ββββ | 402/1200 [01:38<04:00, 3.32it/s] 34%|ββββ | 403/1200 [01:38<03:47, 3.50it/s] 34%|ββββ | 404/1200 [01:39<03:43, 3.57it/s] 34%|ββββ | 405/1200 [01:39<03:38, 3.63it/s] 34%|ββββ | 406/1200 [01:39<03:46, 3.50it/s] 34%|ββββ | 407/1200 [01:39<03:41, 3.58it/s] 34%|ββββ | 408/1200 [01:40<03:38, 3.62it/s] 34%|ββββ | 409/1200 [01:40<03:38, 3.61it/s] 34%|ββββ | 410/1200 [01:40<03:36, 3.64it/s] 34%|ββββ | 411/1200 [01:40<03:28, 3.78it/s] 34%|ββββ | 412/1200 [01:41<03:20, 3.92it/s] 34%|ββββ | 413/1200 [01:41<03:21, 3.90it/s] 34%|ββββ | 414/1200 [01:41<03:19, 3.95it/s] 35%|ββββ | 415/1200 [01:41<03:16, 3.98it/s] 35%|ββββ | 416/1200 [01:42<03:13, 4.04it/s] 35%|ββββ | 417/1200 [01:42<03:19, 3.92it/s] 35%|ββββ | 418/1200 [01:42<03:19, 3.92it/s] 35%|ββββ | 419/1200 [01:42<03:23, 3.85it/s] 35%|ββββ | 420/1200 [01:43<03:18, 3.92it/s] 35%|ββββ | 421/1200 [01:43<03:22, 3.84it/s] 35%|ββββ | 422/1200 [01:43<03:20, 3.88it/s] 35%|ββββ | 423/1200 [01:43<03:17, 3.94it/s] 35%|ββββ | 424/1200 [01:44<03:14, 3.98it/s] 35%|ββββ | 425/1200 [01:44<03:19, 3.88it/s] 36%|ββββ | 426/1200 [01:44<03:19, 3.89it/s] 36%|ββββ | 427/1200 [01:44<03:09, 4.08it/s] 36%|ββββ | 428/1200 [01:45<03:02, 4.22it/s] 36%|ββββ | 429/1200 [01:45<03:10, 4.06it/s] 36%|ββββ | 430/1200 [01:45<03:10, 4.04it/s] 36%|ββββ | 431/1200 [01:45<03:07, 4.11it/s] 36%|ββββ | 432/1200 [01:46<03:17, 3.90it/s] 36%|ββββ | 433/1200 [01:46<03:40, 3.48it/s] 36%|ββββ | 434/1200 [01:46<03:40, 3.47it/s] 36%|ββββ | 435/1200 [01:47<03:28, 3.67it/s] 36%|ββββ | 436/1200 [01:47<03:18, 3.84it/s] 36%|ββββ | 437/1200 [01:47<03:05, 4.11it/s] 36%|ββββ | 438/1200 [01:47<03:07, 4.06it/s] 37%|ββββ | 439/1200 [01:48<03:07, 4.07it/s] 37%|ββββ | 440/1200 [01:48<03:02, 4.16it/s] 37%|ββββ | 441/1200 [01:48<03:06, 4.07it/s] 37%|ββββ | 442/1200 [01:48<03:07, 4.04it/s] 37%|ββββ | 443/1200 [01:49<03:18, 3.81it/s] 37%|ββββ | 444/1200 [01:49<03:19, 3.79it/s] 37%|ββββ | 445/1200 [01:49<03:17, 3.83it/s] 37%|ββββ | 446/1200 [01:49<03:17, 3.82it/s] 37%|ββββ | 447/1200 [01:50<03:28, 3.62it/s] 37%|ββββ | 448/1200 [01:50<03:24, 3.68it/s] 37%|ββββ | 449/1200 [01:50<03:19, 3.77it/s] 38%|ββββ | 450/1200 [01:50<03:18, 3.78it/s] 38%|ββββ | 451/1200 [01:51<03:18, 3.78it/s] 38%|ββββ | 452/1200 [01:51<03:24, 3.66it/s] 38%|ββββ | 453/1200 [01:51<03:19, 3.75it/s] 38%|ββββ | 454/1200 [01:52<03:19, 3.75it/s] 38%|ββββ | 455/1200 [01:52<03:15, 3.81it/s] 38%|ββββ | 456/1200 [01:52<03:07, 3.97it/s] 38%|ββββ | 457/1200 [01:52<03:00, 4.12it/s] 38%|ββββ | 458/1200 [01:52<03:02, 4.08it/s] 38%|ββββ | 459/1200 [01:53<03:33, 3.48it/s] 38%|ββββ | 460/1200 [01:53<03:27, 3.56it/s] 38%|ββββ | 461/1200 [01:53<03:27, 3.56it/s] 38%|ββββ | 462/1200 [01:54<03:27, 3.56it/s] 39%|ββββ | 463/1200 [01:54<03:28, 3.54it/s] 39%|ββββ | 464/1200 [01:54<03:20, 3.67it/s] 39%|ββββ | 465/1200 [01:54<03:12, 3.82it/s] 39%|ββββ | 466/1200 [01:55<03:09, 3.87it/s] 39%|ββββ | 467/1200 [01:55<03:07, 3.92it/s] 39%|ββββ | 468/1200 [01:55<03:06, 3.93it/s] 39%|ββββ | 469/1200 [01:55<03:00, 4.05it/s] 39%|ββββ | 470/1200 [01:56<03:02, 4.01it/s] 39%|ββββ | 471/1200 [01:56<03:05, 3.92it/s] 39%|ββββ | 472/1200 [01:56<02:58, 4.09it/s] 39%|ββββ | 473/1200 [01:56<02:53, 4.20it/s] 40%|ββββ | 474/1200 [01:57<02:51, 4.23it/s] 40%|ββββ | 475/1200 [01:57<02:54, 4.16it/s] 40%|ββββ | 476/1200 [01:57<02:56, 4.10it/s] 40%|ββββ | 477/1200 [01:57<02:54, 4.13it/s] 40%|ββββ | 478/1200 [01:58<03:03, 3.93it/s] 40%|ββββ | 479/1200 [01:58<03:03, 3.93it/s] 40%|ββββ | 480/1200 [01:58<02:52, 4.18it/s] 40%|ββββ | 481/1200 [01:58<02:53, 4.14it/s] 40%|ββββ | 482/1200 [01:59<02:49, 4.22it/s] 40%|ββββ | 483/1200 [01:59<02:49, 4.22it/s] 40%|ββββ | 484/1200 [01:59<02:52, 4.15it/s] 40%|ββββ | 485/1200 [01:59<02:50, 4.19it/s] 40%|ββββ | 486/1200 [02:00<02:52, 4.13it/s] 41%|ββββ | 487/1200 [02:00<02:55, 4.07it/s] 41%|ββββ | 488/1200 [02:00<02:52, 4.13it/s] 41%|ββββ | 489/1200 [02:00<02:50, 4.17it/s] 41%|ββββ | 490/1200 [02:01<02:44, 4.31it/s] 41%|ββββ | 491/1200 [02:01<02:40, 4.42it/s] 41%|ββββ | 492/1200 [02:01<02:37, 4.49it/s] 41%|ββββ | 493/1200 [02:01<02:35, 4.55it/s] 41%|ββββ | 494/1200 [02:01<02:31, 4.66it/s] 41%|βββββ | 495/1200 [02:02<02:29, 4.73it/s] 41%|βββββ | 496/1200 [02:02<02:41, 4.37it/s] 41%|βββββ | 497/1200 [02:02<02:56, 3.99it/s] 42%|βββββ | 498/1200 [02:02<02:49, 4.14it/s] 42%|βββββ | 499/1200 [02:03<02:48, 4.17it/s] 42%|βββββ | 500/1200 [02:03<02:51, 4.08it/s] 42%|βββββ | 501/1200 [02:03<02:49, 4.13it/s] 42%|βββββ | 502/1200 [02:03<02:45, 4.21it/s] 42%|βββββ | 503/1200 [02:04<02:35, 4.47it/s] 42%|βββββ | 504/1200 [02:04<02:33, 4.52it/s] 42%|βββββ | 505/1200 [02:04<02:26, 4.75it/s] 42%|βββββ | 506/1200 [02:04<02:28, 4.68it/s] 42%|βββββ | 507/1200 [02:04<02:28, 4.67it/s] 42%|βββββ | 508/1200 [02:05<02:20, 4.93it/s] 42%|βββββ | 509/1200 [02:05<02:28, 4.66it/s] 42%|βββββ | 510/1200 [02:05<02:29, 4.61it/s] 43%|βββββ | 511/1200 [02:05<02:35, 4.43it/s] 43%|βββββ | 512/1200 [02:05<02:39, 4.32it/s] 43%|βββββ | 513/1200 [02:06<02:44, 4.19it/s] 43%|βββββ | 514/1200 [02:06<02:45, 4.14it/s] 43%|βββββ | 515/1200 [02:06<02:40, 4.27it/s] 43%|βββββ | 516/1200 [02:06<02:35, 4.39it/s] 43%|βββββ | 517/1200 [02:07<02:29, 4.57it/s] 43%|βββββ | 518/1200 [02:07<02:31, 4.51it/s] 43%|βββββ | 519/1200 [02:07<02:29, 4.54it/s] 43%|βββββ | 520/1200 [02:07<02:28, 4.59it/s] 43%|βββββ | 521/1200 [02:07<02:26, 4.64it/s] 44%|βββββ | 522/1200 [02:08<02:21, 4.79it/s] 44%|βββββ | 523/1200 [02:08<02:26, 4.63it/s] 44%|βββββ | 524/1200 [02:08<02:26, 4.62it/s] 44%|βββββ | 525/1200 [02:08<02:21, 4.76it/s] 44%|βββββ | 526/1200 [02:09<02:23, 4.71it/s] 44%|βββββ | 527/1200 [02:09<02:31, 4.45it/s] 44%|βββββ | 528/1200 [02:09<02:34, 4.36it/s] 44%|βββββ | 529/1200 [02:09<02:30, 4.46it/s] 44%|βββββ | 530/1200 [02:09<02:26, 4.58it/s] 44%|βββββ | 531/1200 [02:10<02:30, 4.44it/s] 44%|βββββ | 532/1200 [02:10<02:27, 4.54it/s] 44%|βββββ | 533/1200 [02:10<02:26, 4.57it/s] 44%|βββββ | 534/1200 [02:10<02:27, 4.52it/s] 45%|βββββ | 535/1200 [02:11<02:28, 4.49it/s] 45%|βββββ | 536/1200 [02:11<02:31, 4.38it/s] 45%|βββββ | 537/1200 [02:11<02:31, 4.39it/s] 45%|βββββ | 538/1200 [02:11<02:26, 4.51it/s] 45%|βββββ | 539/1200 [02:12<02:39, 4.14it/s] 45%|βββββ | 540/1200 [02:12<02:33, 4.29it/s] 45%|βββββ | 541/1200 [02:12<02:26, 4.49it/s] 45%|βββββ | 542/1200 [02:12<02:25, 4.51it/s] 45%|βββββ | 543/1200 [02:12<02:32, 4.31it/s] 45%|βββββ | 544/1200 [02:13<02:30, 4.35it/s] 45%|βββββ | 545/1200 [02:13<02:25, 4.49it/s] 46%|βββββ | 546/1200 [02:13<02:26, 4.48it/s] 46%|βββββ | 547/1200 [02:13<02:32, 4.27it/s] 46%|βββββ | 548/1200 [02:14<02:27, 4.43it/s] 46%|βββββ | 549/1200 [02:14<02:21, 4.60it/s] 46%|βββββ | 550/1200 [02:14<02:22, 4.56it/s] 46%|βββββ | 551/1200 [02:14<02:25, 4.47it/s] 46%|βββββ | 552/1200 [02:14<02:24, 4.49it/s] 46%|βββββ | 553/1200 [02:15<02:21, 4.59it/s] 46%|βββββ | 554/1200 [02:15<02:17, 4.69it/s] 46%|βββββ | 555/1200 [02:15<02:24, 4.47it/s] 46%|βββββ | 556/1200 [02:15<02:25, 4.42it/s] 46%|βββββ | 557/1200 [02:15<02:20, 4.57it/s] 46%|βββββ | 558/1200 [02:16<02:16, 4.69it/s] 47%|βββββ | 559/1200 [02:16<02:19, 4.60it/s] 47%|βββββ | 560/1200 [02:16<02:24, 4.42it/s] 47%|βββββ | 561/1200 [02:16<02:21, 4.53it/s] 47%|βββββ | 562/1200 [02:17<02:18, 4.61it/s] 47%|βββββ | 563/1200 [02:17<02:21, 4.51it/s] 47%|βββββ | 564/1200 [02:17<02:22, 4.47it/s] 47%|βββββ | 565/1200 [02:17<02:18, 4.60it/s] 47%|βββββ | 566/1200 [02:18<02:25, 4.35it/s] 47%|βββββ | 567/1200 [02:18<02:31, 4.18it/s] 47%|βββββ | 568/1200 [02:18<02:28, 4.25it/s] 47%|βββββ | 569/1200 [02:18<02:29, 4.23it/s] 48%|βββββ | 570/1200 [02:19<02:37, 4.00it/s] 48%|βββββ | 571/1200 [02:19<02:40, 3.93it/s] 48%|βββββ | 572/1200 [02:19<02:46, 3.77it/s] 48%|βββββ | 573/1200 [02:19<02:54, 3.59it/s] 48%|βββββ | 574/1200 [02:20<03:07, 3.33it/s] 48%|βββββ | 575/1200 [02:20<03:03, 3.41it/s] 48%|βββββ | 576/1200 [02:20<02:55, 3.55it/s] 48%|βββββ | 577/1200 [02:21<02:50, 3.66it/s] 48%|βββββ | 578/1200 [02:21<02:42, 3.83it/s] 48%|βββββ | 579/1200 [02:21<02:44, 3.77it/s] 48%|βββββ | 580/1200 [02:21<02:37, 3.93it/s] 48%|βββββ | 581/1200 [02:21<02:29, 4.15it/s] 48%|βββββ | 582/1200 [02:22<02:27, 4.19it/s] 49%|βββββ | 583/1200 [02:22<02:35, 3.96it/s] 49%|βββββ | 584/1200 [02:22<02:34, 3.99it/s] 49%|βββββ | 585/1200 [02:22<02:34, 3.99it/s] 49%|βββββ | 586/1200 [02:23<02:38, 3.87it/s] 49%|βββββ | 587/1200 [02:23<02:33, 3.99it/s] 49%|βββββ | 588/1200 [02:23<02:26, 4.17it/s] 49%|βββββ | 589/1200 [02:23<02:26, 4.17it/s] 49%|βββββ | 590/1200 [02:24<02:23, 4.24it/s] 49%|βββββ | 591/1200 [02:24<02:19, 4.35it/s] 49%|βββββ | 592/1200 [02:24<02:17, 4.44it/s] 49%|βββββ | 593/1200 [02:24<02:21, 4.28it/s] 50%|βββββ | 594/1200 [02:25<02:20, 4.32it/s] 50%|βββββ | 595/1200 [02:25<02:16, 4.42it/s] 50%|βββββ | 596/1200 [02:25<02:15, 4.45it/s] 50%|βββββ | 597/1200 [02:25<02:16, 4.41it/s] 50%|βββββ | 598/1200 [02:25<02:13, 4.50it/s] 50%|βββββ | 599/1200 [02:26<02:10, 4.59it/s] 50%|βββββ | 600/1200 [02:26<02:08, 4.65it/s] 50%|βββββ | 601/1200 [02:26<02:25, 4.12it/s] 50%|βββββ | 602/1200 [02:26<02:22, 4.19it/s] 50%|βββββ | 603/1200 [02:27<02:16, 4.38it/s] 50%|βββββ | 604/1200 [02:27<02:10, 4.55it/s] 50%|βββββ | 605/1200 [02:27<02:19, 4.26it/s] 50%|βββββ | 606/1200 [02:27<02:23, 4.15it/s] 51%|βββββ | 607/1200 [02:28<02:27, 4.01it/s] 51%|βββββ | 608/1200 [02:28<02:25, 4.06it/s] 51%|βββββ | 609/1200 [02:28<02:23, 4.13it/s] 51%|βββββ | 610/1200 [02:28<02:18, 4.25it/s] 51%|βββββ | 611/1200 [02:29<02:12, 4.44it/s] 51%|βββββ | 612/1200 [02:29<02:12, 4.43it/s] 51%|βββββ | 613/1200 [02:29<02:43, 3.58it/s] 51%|βββββ | 614/1200 [02:29<02:57, 3.31it/s] 51%|ββββββ | 615/1200 [02:30<03:12, 3.03it/s] 51%|ββββββ | 616/1200 [02:30<03:07, 3.11it/s] 51%|ββββββ | 617/1200 [02:31<03:15, 2.99it/s] 52%|ββββββ | 618/1200 [02:31<03:05, 3.14it/s] 52%|ββββββ | 619/1200 [02:31<02:53, 3.35it/s] 52%|ββββββ | 620/1200 [02:31<02:56, 3.29it/s] 52%|ββββββ | 621/1200 [02:32<02:53, 3.33it/s] 52%|ββββββ | 622/1200 [02:32<02:54, 3.30it/s] 52%|ββββββ | 623/1200 [02:32<03:04, 3.12it/s] 52%|ββββββ | 624/1200 [02:33<03:02, 3.16it/s] 52%|ββββββ | 625/1200 [02:33<03:01, 3.18it/s] 52%|ββββββ | 626/1200 [02:33<03:13, 2.97it/s] 52%|ββββββ | 627/1200 [02:34<03:06, 3.07it/s] 52%|ββββββ | 628/1200 [02:34<03:00, 3.16it/s] 52%|ββββββ | 629/1200 [02:34<03:00, 3.16it/s] 52%|ββββββ | 630/1200 [02:35<02:53, 3.28it/s] 53%|ββββββ | 631/1200 [02:35<02:47, 3.40it/s] 53%|ββββββ | 632/1200 [02:35<02:54, 3.25it/s] 53%|ββββββ | 633/1200 [02:35<02:52, 3.28it/s] 53%|ββββββ | 634/1200 [02:36<02:45, 3.43it/s] 53%|ββββββ | 635/1200 [02:36<02:43, 3.45it/s] 53%|ββββββ | 636/1200 [02:36<02:36, 3.60it/s] 53%|ββββββ | 637/1200 [02:37<02:34, 3.64it/s] 53%|ββββββ | 638/1200 [02:37<02:30, 3.73it/s] 53%|ββββββ | 639/1200 [02:37<02:28, 3.79it/s] 53%|ββββββ | 640/1200 [02:37<02:27, 3.80it/s] 53%|ββββββ | 641/1200 [02:38<02:31, 3.70it/s] 54%|ββββββ | 642/1200 [02:38<02:27, 3.80it/s] 54%|ββββββ | 643/1200 [02:38<02:25, 3.83it/s] 54%|ββββββ | 644/1200 [02:38<02:24, 3.85it/s] 54%|ββββββ | 645/1200 [02:39<02:26, 3.80it/s] 54%|ββββββ | 646/1200 [02:39<02:25, 3.81it/s] 54%|ββββββ | 647/1200 [02:39<02:27, 3.76it/s] 54%|ββββββ | 648/1200 [02:39<02:27, 3.75it/s] 54%|ββββββ | 649/1200 [02:40<02:22, 3.87it/s] 54%|ββββββ | 650/1200 [02:40<02:27, 3.73it/s] 54%|ββββββ | 651/1200 [02:40<02:26, 3.75it/s] 54%|ββββββ | 652/1200 [02:40<02:25, 3.76it/s] 54%|ββββββ | 653/1200 [02:41<02:25, 3.76it/s] 55%|ββββββ | 654/1200 [02:41<02:30, 3.62it/s] 55%|ββββββ | 655/1200 [02:41<02:25, 3.75it/s] 55%|ββββββ | 656/1200 [02:42<02:23, 3.79it/s] 55%|ββββββ | 657/1200 [02:42<02:18, 3.91it/s] 55%|ββββββ | 658/1200 [02:42<02:23, 3.77it/s] 55%|ββββββ | 659/1200 [02:42<02:20, 3.85it/s] 55%|ββββββ | 660/1200 [02:43<02:21, 3.81it/s] 55%|ββββββ | 661/1200 [02:43<02:18, 3.88it/s] 55%|ββββββ | 662/1200 [02:43<02:20, 3.83it/s] 55%|ββββββ | 663/1200 [02:43<02:19, 3.84it/s] 55%|ββββββ | 664/1200 [02:44<02:15, 3.96it/s] 55%|ββββββ | 665/1200 [02:44<02:13, 4.00it/s] 56%|ββββββ | 666/1200 [02:44<02:12, 4.02it/s] 56%|ββββββ | 667/1200 [02:44<02:19, 3.81it/s] 56%|ββββββ | 668/1200 [02:45<02:19, 3.82it/s] 56%|ββββββ | 669/1200 [02:45<02:16, 3.89it/s] 56%|ββββββ | 670/1200 [02:45<02:13, 3.98it/s] 56%|ββββββ | 671/1200 [02:45<02:17, 3.85it/s] 56%|ββββββ | 672/1200 [02:46<02:15, 3.89it/s] 56%|ββββββ | 673/1200 [02:46<02:12, 3.98it/s] 56%|ββββββ | 674/1200 [02:46<02:11, 4.01it/s] 56%|ββββββ | 675/1200 [02:46<02:16, 3.84it/s] 56%|ββββββ | 676/1200 [02:47<02:17, 3.82it/s] 56%|ββββββ | 677/1200 [02:47<02:16, 3.84it/s] 56%|ββββββ | 678/1200 [02:47<02:12, 3.94it/s] 57%|ββββββ | 679/1200 [02:47<02:15, 3.85it/s] 57%|ββββββ | 680/1200 [02:48<02:14, 3.86it/s] 57%|ββββββ | 681/1200 [02:48<02:10, 3.98it/s] 57%|ββββββ | 682/1200 [02:48<02:07, 4.06it/s] 57%|ββββββ | 683/1200 [02:48<02:11, 3.92it/s] 57%|ββββββ | 684/1200 [02:49<02:14, 3.85it/s] 57%|ββββββ | 685/1200 [02:49<02:10, 3.94it/s] 57%|ββββββ | 686/1200 [02:49<02:13, 3.85it/s] 57%|ββββββ | 687/1200 [02:49<02:12, 3.87it/s] 57%|ββββββ | 688/1200 [02:50<02:11, 3.91it/s] 57%|ββββββ | 689/1200 [02:50<02:04, 4.10it/s] 57%|ββββββ | 690/1200 [02:50<02:06, 4.03it/s] 58%|ββββββ | 691/1200 [02:50<02:06, 4.03it/s] 58%|ββββββ | 692/1200 [02:51<02:03, 4.12it/s] 58%|ββββββ | 693/1200 [02:51<02:04, 4.08it/s] 58%|ββββββ | 694/1200 [02:51<02:07, 3.98it/s] 58%|ββββββ | 695/1200 [02:51<02:05, 4.03it/s] 58%|ββββββ | 696/1200 [02:52<02:00, 4.17it/s] 58%|ββββββ | 697/1200 [02:52<01:58, 4.23it/s] 58%|ββββββ | 698/1200 [02:52<02:01, 4.13it/s] 58%|ββββββ | 699/1200 [02:52<02:00, 4.14it/s] 58%|ββββββ | 700/1200 [02:53<01:58, 4.22it/s] 58%|ββββββ | 701/1200 [02:53<01:58, 4.23it/s] 58%|ββββββ | 702/1200 [02:53<01:59, 4.15it/s] 59%|ββββββ | 703/1200 [02:53<01:58, 4.18it/s] 59%|ββββββ | 704/1200 [02:54<01:56, 4.26it/s] 59%|ββββββ | 705/1200 [02:54<01:53, 4.36it/s] 59%|ββββββ | 706/1200 [02:54<01:51, 4.41it/s] 59%|ββββββ | 707/1200 [02:54<01:50, 4.48it/s] 59%|ββββββ | 708/1200 [02:54<01:46, 4.62it/s] 59%|ββββββ | 709/1200 [02:55<01:49, 4.49it/s] 59%|ββββββ | 710/1200 [02:55<01:50, 4.44it/s] 59%|ββββββ | 711/1200 [02:55<01:48, 4.50it/s] 59%|ββββββ | 712/1200 [02:55<01:49, 4.46it/s] 59%|ββββββ | 713/1200 [02:56<01:54, 4.26it/s] 60%|ββββββ | 714/1200 [02:56<01:53, 4.29it/s] 60%|ββββββ | 715/1200 [02:56<01:52, 4.30it/s] 60%|ββββββ | 716/1200 [02:56<01:55, 4.20it/s] 60%|ββββββ | 717/1200 [02:57<01:56, 4.14it/s] 60%|ββββββ | 718/1200 [02:57<01:57, 4.11it/s] 60%|ββββββ | 719/1200 [02:57<01:57, 4.08it/s] 60%|ββββββ | 720/1200 [02:57<01:59, 4.03it/s] 60%|ββββββ | 721/1200 [02:58<01:56, 4.10it/s] 60%|ββββββ | 722/1200 [02:58<01:57, 4.08it/s] 60%|ββββββ | 723/1200 [02:58<01:57, 4.05it/s] 60%|ββββββ | 724/1200 [02:58<01:59, 3.98it/s] 60%|ββββββ | 725/1200 [02:59<02:01, 3.91it/s] 60%|ββββββ | 726/1200 [02:59<02:02, 3.88it/s] 61%|ββββββ | 727/1200 [02:59<02:03, 3.82it/s] 61%|ββββββ | 728/1200 [02:59<02:04, 3.79it/s] 61%|ββββββ | 729/1200 [03:00<02:08, 3.67it/s] 61%|ββββββ | 730/1200 [03:00<02:08, 3.66it/s] 61%|ββββββ | 731/1200 [03:00<02:04, 3.78it/s] 61%|ββββββ | 732/1200 [03:00<02:02, 3.83it/s] 61%|ββββββ | 733/1200 [03:01<02:05, 3.73it/s] 61%|ββββββ | 734/1200 [03:01<02:02, 3.79it/s] 61%|βββββββ | 735/1200 [03:01<02:00, 3.87it/s] 61%|βββββββ | 736/1200 [03:01<01:57, 3.95it/s] 61%|βββββββ | 737/1200 [03:02<02:02, 3.78it/s] 62%|βββββββ | 738/1200 [03:02<02:02, 3.77it/s] 62%|βββββββ | 739/1200 [03:02<02:00, 3.82it/s] 62%|βββββββ | 740/1200 [03:03<01:57, 3.93it/s] 62%|βββββββ | 741/1200 [03:03<01:57, 3.91it/s] 62%|βββββββ | 742/1200 [03:03<01:57, 3.90it/s] 62%|βββββββ | 743/1200 [03:03<01:55, 3.95it/s] 62%|βββββββ | 744/1200 [03:04<01:53, 4.01it/s] 62%|βββββββ | 745/1200 [03:04<01:56, 3.90it/s] 62%|βββββββ | 746/1200 [03:04<01:54, 3.95it/s] 62%|βββββββ | 747/1200 [03:04<01:54, 3.95it/s] 62%|βββββββ | 748/1200 [03:05<01:52, 4.02it/s] 62%|βββββββ | 749/1200 [03:05<01:54, 3.95it/s] 62%|βββββββ | 750/1200 [03:05<01:54, 3.91it/s] 63%|βββββββ | 751/1200 [03:05<01:50, 4.05it/s] 63%|βββββββ | 752/1200 [03:06<01:48, 4.12it/s] 63%|βββββββ | 753/1200 [03:06<01:51, 4.01it/s] 63%|βββββββ | 754/1200 [03:06<01:53, 3.94it/s] 63%|βββββββ | 755/1200 [03:06<01:52, 3.97it/s] 63%|βββββββ | 756/1200 [03:07<01:52, 3.94it/s] 63%|βββββββ | 757/1200 [03:07<01:51, 3.96it/s] 63%|βββββββ | 758/1200 [03:07<01:54, 3.85it/s] 63%|βββββββ | 759/1200 [03:07<01:53, 3.87it/s] 63%|βββββββ | 760/1200 [03:08<01:50, 3.97it/s] 63%|βββββββ | 761/1200 [03:08<01:48, 4.05it/s] 64%|βββββββ | 762/1200 [03:08<01:47, 4.06it/s] 64%|βββββββ | 763/1200 [03:08<01:50, 3.97it/s] 64%|βββββββ | 764/1200 [03:09<01:51, 3.89it/s] 64%|βββββββ | 765/1200 [03:09<01:51, 3.89it/s] 64%|βββββββ | 766/1200 [03:09<01:48, 4.01it/s] 64%|βββββββ | 767/1200 [03:09<01:48, 3.98it/s] 64%|βββββββ | 768/1200 [03:10<01:48, 3.98it/s] 64%|βββββββ | 769/1200 [03:10<01:45, 4.08it/s] 64%|βββββββ | 770/1200 [03:10<01:45, 4.06it/s] 64%|βββββββ | 771/1200 [03:10<01:48, 3.96it/s] 64%|βββββββ | 772/1200 [03:11<01:47, 3.98it/s] 64%|βββββββ | 773/1200 [03:11<01:45, 4.06it/s] 64%|βββββββ | 774/1200 [03:11<01:46, 4.02it/s] 65%|βββββββ | 775/1200 [03:11<01:48, 3.93it/s] 65%|βββββββ | 776/1200 [03:12<01:49, 3.88it/s] 65%|βββββββ | 777/1200 [03:12<01:47, 3.94it/s] 65%|βββββββ | 778/1200 [03:12<01:45, 4.01it/s] 65%|βββββββ | 779/1200 [03:12<01:49, 3.86it/s] 65%|βββββββ | 780/1200 [03:13<01:47, 3.92it/s] 65%|βββββββ | 781/1200 [03:13<01:44, 4.01it/s] 65%|βββββββ | 782/1200 [03:13<01:43, 4.05it/s] 65%|βββββββ | 783/1200 [03:13<01:49, 3.80it/s] 65%|βββββββ | 784/1200 [03:14<01:48, 3.82it/s] 65%|βββββββ | 785/1200 [03:14<01:45, 3.94it/s] 66%|βββββββ | 786/1200 [03:14<01:43, 4.00it/s] 66%|βββββββ | 787/1200 [03:14<01:46, 3.87it/s] 66%|βββββββ | 788/1200 [03:15<01:44, 3.96it/s] 66%|βββββββ | 789/1200 [03:15<01:42, 4.03it/s] 66%|βββββββ | 790/1200 [03:15<01:40, 4.10it/s] 66%|βββββββ | 791/1200 [03:15<01:43, 3.95it/s] 66%|βββββββ | 792/1200 [03:16<01:42, 3.96it/s] 66%|βββββββ | 793/1200 [03:16<01:40, 4.06it/s] 66%|βββββββ | 794/1200 [03:16<01:38, 4.13it/s] 66%|βββββββ | 795/1200 [03:16<01:41, 3.99it/s] 66%|βββββββ | 796/1200 [03:17<01:41, 4.00it/s] 66%|βββββββ | 797/1200 [03:17<01:39, 4.06it/s] 66%|βββββββ | 798/1200 [03:17<01:38, 4.09it/s] 67%|βββββββ | 799/1200 [03:17<01:41, 3.96it/s] 67%|βββββββ | 800/1200 [03:18<01:40, 3.96it/s] 67%|βββββββ | 801/1200 [03:18<01:39, 4.01it/s] 67%|βββββββ | 802/1200 [03:18<01:39, 3.99it/s] 67%|βββββββ | 803/1200 [03:18<01:38, 4.03it/s] 67%|βββββββ | 804/1200 [03:19<01:38, 4.02it/s] 67%|βββββββ | 805/1200 [03:19<01:36, 4.08it/s] 67%|βββββββ | 806/1200 [03:19<01:38, 3.99it/s] 67%|βββββββ | 807/1200 [03:19<01:36, 4.07it/s] 67%|βββββββ | 808/1200 [03:20<01:34, 4.13it/s] 67%|βββββββ | 809/1200 [03:20<01:37, 4.01it/s] 68%|βββββββ | 810/1200 [03:20<01:35, 4.09it/s] 68%|βββββββ | 811/1200 [03:20<01:31, 4.25it/s] 68%|βββββββ | 812/1200 [03:21<01:31, 4.26it/s] 68%|βββββββ | 813/1200 [03:21<01:30, 4.26it/s] 68%|βββββββ | 814/1200 [03:21<01:30, 4.28it/s] 68%|βββββββ | 815/1200 [03:21<01:26, 4.43it/s] 68%|βββββββ | 816/1200 [03:21<01:24, 4.54it/s] 68%|βββββββ | 817/1200 [03:22<01:28, 4.33it/s] 68%|βββββββ | 818/1200 [03:22<01:28, 4.30it/s] 68%|βββββββ | 819/1200 [03:22<01:29, 4.26it/s] 68%|βββββββ | 820/1200 [03:22<01:28, 4.28it/s] 68%|βββββββ | 821/1200 [03:23<01:28, 4.28it/s] 68%|βββββββ | 822/1200 [03:23<01:28, 4.26it/s] 69%|βββββββ | 823/1200 [03:23<01:28, 4.27it/s] 69%|βββββββ | 824/1200 [03:23<01:31, 4.10it/s] 69%|βββββββ | 825/1200 [03:24<01:32, 4.05it/s] 69%|βββββββ | 826/1200 [03:24<01:30, 4.14it/s] 69%|βββββββ | 827/1200 [03:24<01:28, 4.20it/s] 69%|βββββββ | 828/1200 [03:24<01:31, 4.06it/s] 69%|βββββββ | 829/1200 [03:25<01:33, 3.98it/s] 69%|βββββββ | 830/1200 [03:25<01:29, 4.13it/s] 69%|βββββββ | 831/1200 [03:25<01:27, 4.22it/s] 69%|βββββββ | 832/1200 [03:25<01:28, 4.14it/s] 69%|βββββββ | 833/1200 [03:26<01:28, 4.13it/s] 70%|βββββββ | 834/1200 [03:26<01:26, 4.21it/s] 70%|βββββββ | 835/1200 [03:26<01:25, 4.29it/s] 70%|βββββββ | 836/1200 [03:26<01:27, 4.16it/s] 70%|βββββββ | 837/1200 [03:26<01:27, 4.13it/s] 70%|βββββββ | 838/1200 [03:27<01:27, 4.14it/s] 70%|βββββββ | 839/1200 [03:27<01:35, 3.78it/s] 70%|βββββββ | 840/1200 [03:27<01:33, 3.84it/s] 70%|βββββββ | 841/1200 [03:28<01:32, 3.89it/s] 70%|βββββββ | 842/1200 [03:28<01:31, 3.91it/s] 70%|βββββββ | 843/1200 [03:28<01:32, 3.86it/s] 70%|βββββββ | 844/1200 [03:28<01:31, 3.90it/s] 70%|βββββββ | 845/1200 [03:29<01:28, 3.99it/s] 70%|βββββββ | 846/1200 [03:29<01:30, 3.92it/s] 71%|βββββββ | 847/1200 [03:29<01:28, 3.97it/s] 71%|βββββββ | 848/1200 [03:29<01:29, 3.94it/s] 71%|βββββββ | 849/1200 [03:30<01:28, 3.96it/s] 71%|βββββββ | 850/1200 [03:30<01:29, 3.91it/s] 71%|βββββββ | 851/1200 [03:30<01:31, 3.82it/s] 71%|βββββββ | 852/1200 [03:30<01:36, 3.61it/s] 71%|βββββββ | 853/1200 [03:31<01:40, 3.46it/s] 71%|βββββββ | 854/1200 [03:31<01:40, 3.43it/s] 71%|ββββββββ | 855/1200 [03:31<01:36, 3.57it/s] 71%|ββββββββ | 856/1200 [03:32<01:35, 3.61it/s] 71%|ββββββββ | 857/1200 [03:32<01:36, 3.57it/s] 72%|ββββββββ | 858/1200 [03:32<01:35, 3.59it/s] 72%|ββββββββ | 859/1200 [03:32<01:34, 3.62it/s] 72%|ββββββββ | 860/1200 [03:33<01:34, 3.58it/s] 72%|ββββββββ | 861/1200 [03:33<01:33, 3.63it/s] 72%|ββββββββ | 862/1200 [03:33<01:34, 3.57it/s] 72%|ββββββββ | 863/1200 [03:33<01:33, 3.60it/s] 72%|ββββββββ | 864/1200 [03:34<01:30, 3.72it/s] 72%|ββββββββ | 865/1200 [03:34<01:27, 3.83it/s] 72%|ββββββββ | 866/1200 [03:34<01:26, 3.86it/s] 72%|ββββββββ | 867/1200 [03:35<01:27, 3.79it/s] 72%|ββββββββ | 868/1200 [03:35<01:26, 3.82it/s] 72%|ββββββββ | 869/1200 [03:35<01:26, 3.85it/s] 72%|ββββββββ | 870/1200 [03:35<01:28, 3.72it/s] 73%|ββββββββ | 871/1200 [03:36<01:38, 3.34it/s] 73%|ββββββββ | 872/1200 [03:36<01:33, 3.50it/s] 73%|ββββββββ | 873/1200 [03:36<01:33, 3.49it/s] 73%|ββββββββ | 874/1200 [03:36<01:31, 3.57it/s] 73%|ββββββββ | 875/1200 [03:37<01:30, 3.59it/s] 73%|ββββββββ | 876/1200 [03:37<01:27, 3.68it/s] 73%|ββββββββ | 877/1200 [03:37<01:30, 3.57it/s] 73%|ββββββββ | 878/1200 [03:38<01:28, 3.63it/s] 73%|ββββββββ | 879/1200 [03:38<01:28, 3.62it/s] 73%|ββββββββ | 880/1200 [03:38<01:35, 3.36it/s] 73%|ββββββββ | 881/1200 [03:39<01:36, 3.32it/s] 74%|ββββββββ | 882/1200 [03:39<01:31, 3.47it/s] 74%|ββββββββ | 883/1200 [03:39<01:26, 3.66it/s] 74%|ββββββββ | 884/1200 [03:39<01:22, 3.84it/s] 74%|ββββββββ | 885/1200 [03:40<01:23, 3.79it/s] 74%|ββββββββ | 886/1200 [03:40<01:21, 3.85it/s] 74%|ββββββββ | 887/1200 [03:40<01:21, 3.83it/s] 74%|ββββββββ | 888/1200 [03:40<01:17, 4.02it/s] 74%|ββββββββ | 889/1200 [03:40<01:17, 4.03it/s] 74%|ββββββββ | 890/1200 [03:41<01:15, 4.10it/s] 74%|ββββββββ | 891/1200 [03:41<01:13, 4.18it/s] 74%|ββββββββ | 892/1200 [03:41<01:12, 4.23it/s] 74%|ββββββββ | 893/1200 [03:41<01:13, 4.19it/s] 74%|ββββββββ | 894/1200 [03:42<01:12, 4.20it/s] 75%|ββββββββ | 895/1200 [03:42<01:11, 4.25it/s] 75%|ββββββββ | 896/1200 [03:42<01:16, 3.96it/s] 75%|ββββββββ | 897/1200 [03:42<01:16, 3.94it/s] 75%|ββββββββ | 898/1200 [03:43<01:13, 4.08it/s] 75%|ββββββββ | 899/1200 [03:43<01:10, 4.30it/s] 75%|ββββββββ | 900/1200 [03:43<01:06, 4.50it/s] 75%|ββββββββ | 901/1200 [03:43<01:06, 4.52it/s] 75%|ββββββββ | 902/1200 [03:44<01:08, 4.33it/s] 75%|ββββββββ | 903/1200 [03:44<01:09, 4.27it/s] 75%|ββββββββ | 904/1200 [03:44<01:08, 4.34it/s] 75%|ββββββββ | 905/1200 [03:44<01:06, 4.45it/s] 76%|ββββββββ | 906/1200 [03:44<01:05, 4.46it/s] 76%|ββββββββ | 907/1200 [03:45<01:08, 4.30it/s] 76%|ββββββββ | 908/1200 [03:45<01:07, 4.33it/s] 76%|ββββββββ | 909/1200 [03:45<01:06, 4.38it/s] 76%|ββββββββ | 910/1200 [03:45<01:06, 4.36it/s] 76%|ββββββββ | 911/1200 [03:46<01:11, 4.04it/s] 76%|ββββββββ | 912/1200 [03:46<01:09, 4.12it/s] 76%|ββββββββ | 913/1200 [03:46<01:07, 4.28it/s] 76%|ββββββββ | 914/1200 [03:46<01:04, 4.42it/s] 76%|ββββββββ | 915/1200 [03:47<01:05, 4.36it/s] 76%|ββββββββ | 916/1200 [03:47<01:07, 4.22it/s] 76%|ββββββββ | 917/1200 [03:47<01:06, 4.28it/s] 76%|ββββββββ | 918/1200 [03:47<01:05, 4.30it/s] 77%|ββββββββ | 919/1200 [03:47<01:04, 4.36it/s] 77%|ββββββββ | 920/1200 [03:48<01:05, 4.26it/s] 77%|ββββββββ | 921/1200 [03:48<01:03, 4.36it/s] 77%|ββββββββ | 922/1200 [03:48<01:04, 4.28it/s] 77%|ββββββββ | 923/1200 [03:48<01:05, 4.25it/s] 77%|ββββββββ | 924/1200 [03:49<01:06, 4.17it/s] 77%|ββββββββ | 925/1200 [03:49<01:07, 4.05it/s] 77%|ββββββββ | 926/1200 [03:49<01:04, 4.27it/s] 77%|ββββββββ | 927/1200 [03:49<01:04, 4.21it/s] 77%|ββββββββ | 928/1200 [03:50<01:03, 4.30it/s] 77%|ββββββββ | 929/1200 [03:50<01:00, 4.51it/s] 78%|ββββββββ | 930/1200 [03:50<00:59, 4.54it/s] 78%|ββββββββ | 931/1200 [03:50<01:00, 4.41it/s] 78%|ββββββββ | 932/1200 [03:50<00:59, 4.49it/s] 78%|ββββββββ | 933/1200 [03:51<00:57, 4.61it/s] 78%|ββββββββ | 934/1200 [03:51<00:56, 4.71it/s] 78%|ββββββββ | 935/1200 [03:51<00:59, 4.47it/s] 78%|ββββββββ | 936/1200 [03:51<00:56, 4.64it/s] 78%|ββββββββ | 937/1200 [03:52<00:55, 4.78it/s] 78%|ββββββββ | 938/1200 [03:52<00:52, 4.95it/s] 78%|ββββββββ | 939/1200 [03:52<00:53, 4.89it/s] 78%|ββββββββ | 940/1200 [03:52<00:55, 4.72it/s] 78%|ββββββββ | 941/1200 [03:52<00:54, 4.78it/s] 78%|ββββββββ | 942/1200 [03:53<00:53, 4.84it/s] 79%|ββββββββ | 943/1200 [03:53<00:55, 4.65it/s] 79%|ββββββββ | 944/1200 [03:53<00:54, 4.69it/s] 79%|ββββββββ | 945/1200 [03:53<00:52, 4.87it/s] 79%|ββββββββ | 946/1200 [03:53<00:51, 4.89it/s] 79%|ββββββββ | 947/1200 [03:54<00:52, 4.84it/s] 79%|ββββββββ | 948/1200 [03:54<00:54, 4.59it/s] 79%|ββββββββ | 949/1200 [03:54<00:52, 4.76it/s] 79%|ββββββββ | 950/1200 [03:54<00:51, 4.89it/s] 79%|ββββββββ | 951/1200 [03:54<00:54, 4.61it/s] 79%|ββββββββ | 952/1200 [03:55<00:54, 4.56it/s] 79%|ββββββββ | 953/1200 [03:55<00:52, 4.71it/s] 80%|ββββββββ | 954/1200 [03:55<00:52, 4.70it/s] 80%|ββββββββ | 955/1200 [03:55<00:54, 4.51it/s] 80%|ββββββββ | 956/1200 [03:56<00:53, 4.56it/s] 80%|ββββββββ | 957/1200 [03:56<00:52, 4.67it/s] 80%|ββββββββ | 958/1200 [03:56<00:50, 4.75it/s] 80%|ββββββββ | 959/1200 [03:56<00:51, 4.72it/s] 80%|ββββββββ | 960/1200 [03:56<00:50, 4.72it/s] 80%|ββββββββ | 961/1200 [03:57<00:48, 4.92it/s] 80%|ββββββββ | 962/1200 [03:57<00:47, 5.06it/s] 80%|ββββββββ | 963/1200 [03:57<00:47, 4.95it/s] 80%|ββββββββ | 964/1200 [03:57<00:46, 5.11it/s] 80%|ββββββββ | 965/1200 [03:57<00:43, 5.41it/s] 80%|ββββββββ | 966/1200 [03:58<00:42, 5.45it/s] 81%|ββββββββ | 967/1200 [03:58<00:43, 5.38it/s] 81%|ββββββββ | 968/1200 [03:58<00:46, 4.99it/s] 81%|ββββββββ | 969/1200 [03:58<00:44, 5.20it/s] 81%|ββββββββ | 970/1200 [03:58<00:43, 5.30it/s] 81%|ββββββββ | 971/1200 [03:58<00:43, 5.32it/s] 81%|ββββββββ | 972/1200 [03:59<00:43, 5.26it/s] 81%|ββββββββ | 973/1200 [03:59<00:45, 5.04it/s] 81%|ββββββββ | 974/1200 [03:59<00:44, 5.13it/s] 81%|βββββββββ | 975/1200 [03:59<00:43, 5.16it/s] 81%|βββββββββ | 976/1200 [03:59<00:43, 5.19it/s] 81%|βββββββββ | 977/1200 [04:00<00:44, 5.05it/s] 82%|βββββββββ | 978/1200 [04:00<00:43, 5.11it/s] 82%|βββββββββ | 979/1200 [04:00<00:43, 5.03it/s] 82%|βββββββββ | 980/1200 [04:00<00:42, 5.18it/s] 82%|βββββββββ | 981/1200 [04:00<00:43, 5.04it/s] 82%|βββββββββ | 982/1200 [04:01<00:44, 4.95it/s] 82%|βββββββββ | 983/1200 [04:01<00:42, 5.08it/s] 82%|βββββββββ | 984/1200 [04:01<00:41, 5.18it/s] 82%|βββββββββ | 985/1200 [04:01<00:42, 5.10it/s] 82%|βββββββββ | 986/1200 [04:01<00:42, 5.02it/s] 82%|βββββββββ | 987/1200 [04:02<00:41, 5.16it/s] 82%|βββββββββ | 988/1200 [04:02<00:40, 5.27it/s] 82%|βββββββββ | 989/1200 [04:02<00:41, 5.11it/s] 82%|βββββββββ | 990/1200 [04:02<00:42, 4.89it/s] 83%|βββββββββ | 991/1200 [04:02<00:41, 5.01it/s] 83%|βββββββββ | 992/1200 [04:03<00:40, 5.12it/s] 83%|βββββββββ | 993/1200 [04:03<00:43, 4.81it/s] 83%|βββββββββ | 994/1200 [04:03<00:46, 4.46it/s] 83%|βββββββββ | 995/1200 [04:03<00:44, 4.57it/s] 83%|βββββββββ | 996/1200 [04:04<00:44, 4.59it/s] 83%|βββββββββ | 997/1200 [04:04<00:43, 4.64it/s] 83%|βββββββββ | 998/1200 [04:04<00:44, 4.58it/s] 83%|βββββββββ | 999/1200 [04:04<00:44, 4.51it/s] 83%|βββββββββ | 1000/1200 [04:04<00:44, 4.50it/s] 83%|βββββββββ | 1001/1200 [04:05<00:46, 4.31it/s] 84%|βββββββββ | 1002/1200 [04:05<00:43, 4.51it/s] 84%|βββββββββ | 1003/1200 [04:05<00:41, 4.69it/s] 84%|βββββββββ | 1004/1200 [04:05<00:42, 4.60it/s] 84%|βββββββββ | 1005/1200 [04:06<00:45, 4.32it/s] 84%|βββββββββ | 1006/1200 [04:06<00:42, 4.53it/s] 84%|βββββββββ | 1007/1200 [04:06<00:41, 4.66it/s] 84%|βββββββββ | 1008/1200 [04:06<00:41, 4.58it/s] 84%|βββββββββ | 1009/1200 [04:06<00:41, 4.57it/s] 84%|βββββββββ | 1010/1200 [04:07<00:43, 4.33it/s] 84%|βββββββββ | 1011/1200 [04:07<00:43, 4.37it/s] 84%|βββββββββ | 1012/1200 [04:07<00:40, 4.59it/s] 84%|βββββββββ | 1013/1200 [04:07<00:42, 4.35it/s] 84%|βββββββββ | 1014/1200 [04:08<00:42, 4.36it/s] 85%|βββββββββ | 1015/1200 [04:08<00:40, 4.59it/s] 85%|βββββββββ | 1016/1200 [04:08<00:41, 4.41it/s] 85%|βββββββββ | 1017/1200 [04:08<00:41, 4.42it/s] 85%|βββββββββ | 1018/1200 [04:08<00:42, 4.29it/s] 85%|βββββββββ | 1019/1200 [04:09<00:39, 4.53it/s] 85%|βββββββββ | 1020/1200 [04:09<00:38, 4.71it/s] 85%|βββββββββ | 1021/1200 [04:09<00:40, 4.40it/s] 85%|βββββββββ | 1022/1200 [04:09<00:39, 4.47it/s] 85%|βββββββββ | 1023/1200 [04:10<00:38, 4.60it/s] 85%|βββββββββ | 1024/1200 [04:10<00:38, 4.62it/s] 85%|βββββββββ | 1025/1200 [04:10<00:39, 4.47it/s] 86%|βββββββββ | 1026/1200 [04:10<00:38, 4.56it/s] 86%|βββββββββ | 1027/1200 [04:10<00:37, 4.62it/s] 86%|βββββββββ | 1028/1200 [04:11<00:38, 4.41it/s] 86%|βββββββββ | 1029/1200 [04:11<00:38, 4.41it/s] 86%|βββββββββ | 1030/1200 [04:11<00:38, 4.38it/s] 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[MoviePy] Done. [MoviePy] >>>> Video ready: harder_challenge_out.mp4 CPU times: user 7min 12s, sys: 1min 2s, total: 8min 14s Wall time: 4min 53s
In [644]:
clip3= VideoFileClip("../CarND-Advanced-Lane-Lines/challenge_video.mp4")
white_output = 'challenge_out.mp4'
white_clip = clip3.fl_image(pipeline)
%time white_clip.write_videofile(white_output, audio=False)
[MoviePy] >>>> Building video challenge_out.mp4 [MoviePy] Writing video challenge_out.mp4
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[MoviePy] Done. [MoviePy] >>>> Video ready: challenge_out.mp4 CPU times: user 2min 38s, sys: 17.2 s, total: 2min 56s Wall time: 1min 33s
Outcomes¶
In the current version of the project I'm applying basic steps in order to detect lane lines on the provided images and videos.
Current implementation is not robust and require improvements in image preprocessing (finding the best combination of thresholded gradient and color scheme binary images) as well as optimization of lines detection.
Now I'm searching the lane lines from scratch on each image. Tracking the lines shoould improve robustness of the model and most importantly imrpove the performance of the model.
I've mentioned that it takes significant amount of time to process the video which couldn't work in real implementation. After I get the appropriate level of lines detection, I'm going to investigate time consumption on each preprocessing step in order to find the ways to decrease the time of video (or stream) processing.
In [ ]: